diff --git a/Audi_Classification_ML/.ipynb_checkpoints/cleanup-checkpoint.ipynb b/Audi_Classification_ML/.ipynb_checkpoints/cleanup-checkpoint.ipynb
new file mode 100644
index 0000000..517b3f7
--- /dev/null
+++ b/Audi_Classification_ML/.ipynb_checkpoints/cleanup-checkpoint.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "from tqdm import tqdm\n",
+    "from matplotlib import pyplot as plt\n",
+    "import eda\n",
+    "from python_speech_features import mfcc, logfbank\n",
+    "from scipy.io import wavfile\n",
+    "import librosa\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            fname      label\n",
+      "0    5388d14d.wav  Saxophone\n",
+      "1    c685f05f.wav  Saxophone\n",
+      "2    36d20ab5.wav  Saxophone\n",
+      "3    d6665734.wav  Saxophone\n",
+      "4    7352e28f.wav  Saxophone\n",
+      "..            ...        ...\n",
+      "295  3c713bcf.wav   Clarinet\n",
+      "296  2fc00271.wav   Clarinet\n",
+      "297  b0c06255.wav   Clarinet\n",
+      "298  71c6451f.wav   Clarinet\n",
+      "299  5de123c3.wav   Clarinet\n",
+      "\n",
+      "[300 rows x 2 columns]\n",
+      "          fname      label\n",
+      "0  5388d14d.wav  Saxophone\n",
+      "1  c685f05f.wav  Saxophone\n",
+      "2  36d20ab5.wav  Saxophone\n",
+      "3  d6665734.wav  Saxophone\n",
+      "4  7352e28f.wav  Saxophone\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join('data', 'instruments.csv'))\n",
+    "print(df)\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.set_index('fname', inplace=True) # Set our index to be the fname collumn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  label\n",
+      "fname                  \n",
+      "5388d14d.wav  Saxophone\n",
+      "c685f05f.wav  Saxophone\n",
+      "36d20ab5.wav  Saxophone\n",
+      "d6665734.wav  Saxophone\n",
+      "7352e28f.wav  Saxophone\n",
+      "...                 ...\n",
+      "3c713bcf.wav   Clarinet\n",
+      "2fc00271.wav   Clarinet\n",
+      "b0c06255.wav   Clarinet\n",
+      "71c6451f.wav   Clarinet\n",
+      "5de123c3.wav   Clarinet\n",
+      "\n",
+      "[300 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the rate and signal for each of our wavfiles\n",
+    "# and add their ratio to our df as a length collumn/feature\n",
+    "for f in df.index:\n",
+    "    rate, signal = wavfile.read('data/wavfiles/'+f)\n",
+    "    df.at[f,'length'] = signal.shape[0] / rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  label  length\n",
+      "fname                          \n",
+      "5388d14d.wav  Saxophone    4.14\n",
+      "c685f05f.wav  Saxophone    1.54\n",
+      "36d20ab5.wav  Saxophone    4.90\n",
+      "d6665734.wav  Saxophone   10.50\n",
+      "7352e28f.wav  Saxophone    6.24\n",
+      "...                 ...     ...\n",
+      "3c713bcf.wav   Clarinet    6.14\n",
+      "2fc00271.wav   Clarinet    4.20\n",
+      "b0c06255.wav   Clarinet    4.08\n",
+      "71c6451f.wav   Clarinet    3.56\n",
+      "5de123c3.wav   Clarinet    3.34\n",
+      "\n",
+      "[300 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a class list and class distribution\n",
+    "classes = list(np.unique(df.label))\n",
+    "class_dist = df.groupby(['label'])['length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label\n",
+      "Acoustic_guitar     6.948667\n",
+      "Bass_drum           1.937333\n",
+      "Cello               5.000667\n",
+      "Clarinet            6.596000\n",
+      "Double_bass         3.206000\n",
+      "Flute               8.054667\n",
+      "Hi-hat              3.357333\n",
+      "Saxophone           7.124000\n",
+      "Snare_drum          3.987333\n",
+      "Violin_or_fiddle    4.530000\n",
+      "Name: length, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(class_dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.set_title ('class_distribution', y = 1.08) # 1.08 to move title up slightly\n",
+    "\n",
+    "# Setup pie chart with autopct to round off floats\n",
+    "ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',\n",
+    "      shadow=False, startangle=90)\n",
+    "\n",
+    "ax.axis('equal') # Make the pie chart a circle rather than elipse\n",
+    "\n",
+    "plt.show()\n",
+    "df.reset_index(inplace=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we have an idea of how the classes are distribution. We can see the bass drum constitues a disproportionately small amount of the data. In order to even out the data we would have to throw out a lot of data which is not ideal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "signals = {}\n",
+    "fft = {}\n",
+    "fbank = {}\n",
+    "mfccs = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_fft(y, rate):\n",
+    "    n = len(y)\n",
+    "    freq = np.fft.rfftfreq(n, d = 1/rate) # d is our spacing between signal (time that passes between each sample)\n",
+    "    Y = abs(np.fft.rfft(y) / n) # Find the magnitude of our fft, scaled by length of signal\n",
+    "    return(Y, freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab a sample for each class then calculate its signal, fft, fbank, and mfcc\n",
+    "for c in classes:\n",
+    "    wav_file = df[df.label==c].iloc[0,0] # Use iloc to select by position\n",
+    "    signal, rate = librosa.load('data/wavfiles/'+wav_file, sr=44100) # Read in signal and rate with sr coming from scipy.io\n",
+    "    signals[c] = signal\n",
+    "    fft[c] = calc_fft(signal, rate)\n",
+    "    \n",
+    "    bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T # Get nfft with sr / 40\n",
+    "    fbank[c] = bank\n",
+    "    mel = mfcc(signal[:rate], rate, numcep=13, nfft = 1103).T\n",
+    "    mfccs[c] = mel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_signals(signals)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fft(fft)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fbank(fbank)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_mfccs(mfccs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at our time series, we can see a lot of empty space which we can remove later. Looking at Filter Banks and MFCCs we can start to tell each apart. We now create a function to find the envelope of a wave, an example of which we see here. This can help by ignoring, for the most part, portions where the values fall to zero.\n",
+    "\n",
+    "![](figures/signal_envelopes.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def envelope(y, rate, threshold):\n",
+    "    mask = []\n",
+    "    y = pd.Series(y).apply(np.abs) # Convert to series and apply absolute value to deal with negative signal portions\n",
+    "    y_mean = y.rolling(window=int(rate/10), min_periods=1, center=True).mean() # Get rolling average\n",
+    "    for mean in y_mean:\n",
+    "        if mean > threshold:\n",
+    "            mask.append(True)\n",
+    "        else:\n",
+    "            mask.append(False)\n",
+    "    return mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "signals = {}\n",
+    "fft = {}\n",
+    "fbank = {}\n",
+    "mfccs = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab a sample for each class then calculate its signal, fft, fbank, and mfcc this time with the mask\n",
+    "for c in classes:\n",
+    "    wav_file = df[df.label==c].iloc[0,0] # Use iloc to select by position\n",
+    "    signal, rate = librosa.load('data/wavfiles/'+wav_file, sr=44100) # Read in signal and rate with sr coming from scipy.io\n",
+    "    mask = envelope(signal, rate, 0.0005)\n",
+    "    signal = signal[mask]\n",
+    "    signals[c] = signal\n",
+    "    fft[c] = calc_fft(signal, rate)\n",
+    "    \n",
+    "    bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T # Get nfft with sr / 40\n",
+    "    fbank[c] = bank\n",
+    "    mel = mfcc(signal[:rate], rate, numcep=13, nfft = 1103).T\n",
+    "    mfccs[c] = mel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_signals(signals)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fft(fft)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fbank(fbank)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_mfccs(mfccs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can see a lot of the dead space, such as the gaps in flute, has been removed. We can now apply this concept to all of our data and save it in our 'clean' folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(os.listdir('data/clean')) == 0:\n",
+    "    for f in tqdm(df.fname):\n",
+    "        signal, rate = librosa.load('data/wavfiles/'+f, sr=16000) # Load in signal and rate\n",
+    "        mask = envelope(signal, rate, 0.0005) # Create envelope\n",
+    "        wavfile.write(filename='data/clean/'+f, rate=rate, data=signal[mask]) # Write in wav file with signal indexed by mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Audi_Classification_ML/.ipynb_checkpoints/model-checkpoint.ipynb b/Audi_Classification_ML/.ipynb_checkpoints/model-checkpoint.ipynb
new file mode 100644
index 0000000..82d26ea
--- /dev/null
+++ b/Audi_Classification_ML/.ipynb_checkpoints/model-checkpoint.ipynb
@@ -0,0 +1,917 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from scipy.io import wavfile\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM\n",
+    "from keras.layers import Dropout, Dense, TimeDistributed\n",
+    "from keras.models import Sequential\n",
+    "from keras.utils import to_categorical\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from sklearn.utils.class_weight import compute_class_weight\n",
+    "from tqdm import tqdm\n",
+    "from python_speech_features import mfcc\n",
+    "from cfg import config\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/instruments.csv') # Read in our CSV file\n",
+    "df.set_index('fname', inplace=True) # Set the fname collumn as our index\n",
+    "\n",
+    "for f in df.index:\n",
+    "    rate, signal = wavfile.read('data/clean/'+f) # Grab rate and signal from each file\n",
+    "    df.at[f,'length'] = signal.shape[0] / rate # Set the length of each file based on signal and rate\n",
+    "\n",
+    "classes = list(np.unique(df.label)) # Grab classes\n",
+    "class_dist = df.groupby(['label'])['length'].mean() # create a distribution of the classes based on the mean number of each"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot class distribution\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.set_title('Class Distribution', y=1.08)\n",
+    "ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',\n",
+    "       shadow=False, startangle=90)\n",
+    "ax.axis('equal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_samples = 2 * int(df['length'].sum() / 0.1) # Set number of samples to be 20% of the length in time of all data\n",
+    "prob_dist = class_dist / class_dist.sum() # Convert to range of [0,1]\n",
+    "choices = np.random.choice(class_dist.index, p=prob_dist) # Choose a class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = config(mode='conv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_data():\n",
+    "    # If there is data, load it in \n",
+    "    if os.path.isfile(config.p_path):\n",
+    "        print('Loading existing data for {} model'.format(config.mode))\n",
+    "        with open(config.p_path, 'rb') as handle:\n",
+    "            tmp = pickle.load(handle)\n",
+    "            return tmp\n",
+    "    else:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_rand_feat():\n",
+    "    \"\"\"\n",
+    "    This function creates a randomly generated and normalized 4-D feature vector\n",
+    "    \"\"\"\n",
+    "    tmp = check_data() # Load in old pickle data if it exists\n",
+    "    if tmp:\n",
+    "        print(config.mode)\n",
+    "        print(config.p_path)\n",
+    "        return tmp.data[0], tmp.data[1] # Returns our previously built randomized feature\n",
+    "    X = []\n",
+    "    y = []\n",
+    "    _min, _max = float('inf'), -float('inf')\n",
+    "    for _ in tqdm(range(n_samples)):\n",
+    "        rand_class = np.random.choice(class_dist.index, p=prob_dist) # Pick a random class based on our prob_dist probabilities\n",
+    "        file = np.random.choice(df[df.label==rand_class].index) # Choose a random file of class rand_class and get its index\n",
+    "        rate, wav = wavfile.read('data/clean/'+file)\n",
+    "        label = df.at[file,'label']\n",
+    "        rand_index = np.random.randint(0, wav.shape[0] - config.step) # Go to random time point in audio file\n",
+    "        sample = wav[rand_index:rand_index + config.step] # Grab a portion of our wav file at rand_index time point\n",
+    "        X_sample = mfcc(sample, rate,\n",
+    "                       numcep=config.nfeat, nfilt=config.nfilt, nfft = config.nfft)\n",
+    "        _min = min(np.amin(X_sample), _min) # Update min if our sample min is smaller\n",
+    "        _max = max(np.amax(X_sample), _max) # Update max if our sample max is larger\n",
+    "        X.append(X_sample) # Add our randomly selected sample to list X\n",
+    "        y.append(classes.index(label)) # Add the index corrosponding to our sample's label to list y\n",
+    "    config.min = _min # Save min\n",
+    "    config.max = _max # Save max\n",
+    "    X, y = np.array(X), np.array(y) \n",
+    "    X = (X - _min) / (_max - _min) # Normalize our input matrix X based on min and range, possible to normalize based on mean and standard deviation as well\n",
+    "    if config.mode == 'conv':\n",
+    "        X = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1)\n",
+    "    elif config.mode=='time':\n",
+    "        X = X.reshape(X.shape[0], X.shape[1], X.shape[2])\n",
+    "    y = to_categorical(y, num_classes = 10)\n",
+    "    config.data = (X, y) # Store the data\n",
+    "    \n",
+    "    with open(config.p_path, 'wb') as handle:\n",
+    "        pickle.dump(config, handle, protocol=2)\n",
+    "        \n",
+    "    return X, y\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_conv_model():\n",
+    "    model = Sequential()\n",
+    "    # Note that we only use a tenth of a second of our data, so we don't want to pool too much\n",
+    "    model.add(Conv2D(16, (3,3), activation='relu', strides=(1,1), # Strides only (1,1) due to small input space\n",
+    "                    padding='same', input_shape=input_shape))\n",
+    "    model.add(Conv2D(32, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(Conv2D(64, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(Conv2D(128, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(MaxPool2D((2,2), dim_ordering=\"th\"))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dense(64, activation='relu'))\n",
+    "    model.add(Dense(32, activation='relu'))\n",
+    "    model.add(Dense(10, activation='softmax')) # Final layer activate with softmax due to our categorical cross-entropy method\n",
+    "    model.summary()\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                 optimizer='adam',\n",
+    "                 metrics=['acc'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_recurrent_model():\n",
+    "    # Shape of data for RNN is (n,time,feat)\n",
+    "    model = Sequential()\n",
+    "    model.add(LSTM(128, return_sequences=True, input_shape=input_shape))\n",
+    "    model.add(LSTM(128, return_sequences=True))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(TimeDistributed(Dense(64, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(32, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(16, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(8, activation='relu')))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "    model.summary()\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                 optimizer='adam',\n",
+    "                 metrics=['acc'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading existing data for conv model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D((2, 2), data_format=\"channels_first\")`\n",
+      "  if sys.path[0] == '':\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_1 (Conv2D)            (None, 9, 13, 16)         160       \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 9, 13, 32)         4640      \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 9, 13, 64)         18496     \n",
+      "_________________________________________________________________\n",
+      "conv2d_4 (Conv2D)            (None, 9, 13, 128)        73856     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 9, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 9, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 3456)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 128)               442496    \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 64)                8256      \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 32)                2080      \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 10)                330       \n",
+      "=================================================================\n",
+      "Total params: 550,314\n",
+      "Trainable params: 550,314\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Train on 23769 samples, validate on 2641 samples\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00001: val_acc improved from -inf to 0.75615, saving model to models\\conv.model\n",
+      "Epoch 2/10\n"
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+      "\n",
+      "Epoch 00002: val_acc improved from 0.75615 to 0.83643, saving model to models\\conv.model\n",
+      "Epoch 3/10\n"
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+      "\n",
+      "Epoch 00003: val_acc improved from 0.83643 to 0.89663, saving model to models\\conv.model\n",
+      "Epoch 4/10\n"
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+      "\n",
+      "Epoch 00004: val_acc improved from 0.89663 to 0.92389, saving model to models\\conv.model\n",
+      "Epoch 5/10\n"
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+      "\n",
+      "Epoch 00005: val_acc improved from 0.92389 to 0.92616, saving model to models\\conv.model\n",
+      "Epoch 6/10\n"
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+      "\n",
+      "Epoch 00006: val_acc improved from 0.92616 to 0.92881, saving model to models\\conv.model\n",
+      "Epoch 7/10\n"
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+      "\n",
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+      "Epoch 9/10\n"
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+      "\n",
+      "Epoch 00009: val_acc improved from 0.94320 to 0.95229, saving model to models\\conv.model\n",
+      "Epoch 10/10\n"
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00010: val_acc did not improve from 0.95229\n"
+     ]
+    }
+   ],
+   "source": [
+    "if config.mode == 'conv':\n",
+    "    X, y = build_rand_feat()\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2], 1) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_conv_model()\n",
+    "elif config.mode == 'time':\n",
+    "    X, y = build_rand_feat()\n",
+    "    \"\"\"\n",
+    "    TODO\n",
+    "    \n",
+    "    our rand_feat is saved as a pickle file and needs to be reshaped if\n",
+    "    it was originally made for conv model (similar if running conv and \n",
+    "    need to work with time)\n",
+    "    \"\"\"\n",
+    "    X = X.reshape(X.shape[0], X.shape[1], X.shape[2])\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2]) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_recurrent_model()\n",
+    "    \n",
+    "# Setup weights so that our gradient updates based on our class distribution \n",
+    "# (less bass drumbs => stronger gradient from these samples)\n",
+    "# This will give a little extra accuracy as well as reducing bias\n",
+    "class_weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)\n",
+    "\n",
+    "checkpoint = ModelCheckpoint(config.model_path, monitor='val_acc', verbose=1, mode='max',\n",
+    "                            save_best_only=True, save_weights_only=False, period=1)\n",
+    "\n",
+    "# Fit our model\n",
+    "model.fit(X, y, epochs=10, \n",
+    "         shuffle=True,\n",
+    "         class_weight=class_weight,\n",
+    "         validation_split=0.1,\n",
+    "         callbacks=[checkpoint])\n",
+    "\n",
+    "model.save(config.model_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config.mode = 'time'\n",
+    "config.model_path = os.path.join('models', config.mode + '.model')\n",
+    "config.p_path = os.path.join('pickles', config.mode + '.p')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████| 26410/26410 [02:01<00:00, 217.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(26410, 9, 13)\n",
+      "Model: \"sequential_7\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_11 (LSTM)               (None, 9, 128)            72704     \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 9, 128)            131584    \n",
+      "_________________________________________________________________\n",
+      "dropout_7 (Dropout)          (None, 9, 128)            0         \n",
+      "_________________________________________________________________\n",
+      "time_distributed_21 (TimeDis (None, 9, 64)             8256      \n",
+      "_________________________________________________________________\n",
+      "time_distributed_22 (TimeDis (None, 9, 32)             2080      \n",
+      "_________________________________________________________________\n",
+      "time_distributed_23 (TimeDis (None, 9, 16)             528       \n",
+      "_________________________________________________________________\n",
+      "time_distributed_24 (TimeDis (None, 9, 8)              136       \n",
+      "_________________________________________________________________\n",
+      "flatten_7 (Flatten)          (None, 72)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_34 (Dense)             (None, 10)                730       \n",
+      "=================================================================\n",
+      "Total params: 216,018\n",
+      "Trainable params: 216,018\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Train on 23769 samples, validate on 2641 samples\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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val_acc: 0.5112\n",
+      "\n",
+      "Epoch 00001: val_acc improved from -inf to 0.51117, saving model to models\\time.model\n",
+      "Epoch 2/10\n"
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+      "\n",
+      "Epoch 00002: val_acc improved from 0.51117 to 0.68648, saving model to models\\time.model\n",
+      "Epoch 3/10\n"
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val_acc: 0.7429\n",
+      "\n",
+      "Epoch 00003: val_acc improved from 0.68648 to 0.74290, saving model to models\\time.model\n",
+      "Epoch 4/10\n"
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+      "\n",
+      "Epoch 00004: val_acc did not improve from 0.74290\n",
+      "Epoch 5/10\n"
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+      "\n",
+      "Epoch 00005: val_acc improved from 0.74290 to 0.82431, saving model to models\\time.model\n",
+      "Epoch 6/10\n"
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val_acc: 0.8402\n",
+      "\n",
+      "Epoch 00006: val_acc improved from 0.82431 to 0.84021, saving model to models\\time.model\n",
+      "Epoch 7/10\n"
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+      "\n",
+      "Epoch 00007: val_acc did not improve from 0.84021\n",
+      "Epoch 8/10\n"
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+      "\n",
+      "Epoch 00008: val_acc did not improve from 0.84021\n",
+      "Epoch 9/10\n"
+     ]
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val_acc: 0.8633\n",
+      "\n",
+      "Epoch 00009: val_acc improved from 0.84021 to 0.86331, saving model to models\\time.model\n",
+      "Epoch 10/10\n"
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00010: val_acc improved from 0.86331 to 0.87315, saving model to models\\time.model\n"
+     ]
+    }
+   ],
+   "source": [
+    "if config.mode == 'conv':\n",
+    "    X, y = build_rand_feat()\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2], 1) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_conv_model()\n",
+    "elif config.mode == 'time':\n",
+    "    X, y = build_rand_feat()\n",
+    "    print(X.shape)\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2]) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_recurrent_model()\n",
+    "    \n",
+    "# Setup weights so that our gradient updates based on our class distribution \n",
+    "# (less bass drumbs => stronger gradient from these samples)\n",
+    "# This will give a little extra accuracy as well as reducing bias\n",
+    "class_weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)\n",
+    "\n",
+    "checkpoint = ModelCheckpoint(config.model_path, monitor='val_acc', verbose=1, mode='max',\n",
+    "                            save_best_only=True, save_weights_only=False, period=1)\n",
+    "\n",
+    "# Fit our model\n",
+    "model.fit(X, y, epochs=10, \n",
+    "         shuffle=True,\n",
+    "         class_weight=class_weight,\n",
+    "         validation_split=0.1,\n",
+    "         callbacks=[checkpoint])\n",
+    "\n",
+    "model.save(config.model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Audi_Classification_ML/.ipynb_checkpoints/predict-checkpoint.ipynb b/Audi_Classification_ML/.ipynb_checkpoints/predict-checkpoint.ipynb
new file mode 100644
index 0000000..8de89ca
--- /dev/null
+++ b/Audi_Classification_ML/.ipynb_checkpoints/predict-checkpoint.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from tqdm import tqdm\n",
+    "from scipy.io import wavfile\n",
+    "from python_speech_features import mfcc\n",
+    "from keras.models import load_model\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_predictions(audio_dir):\n",
+    "    \"\"\"\n",
+    "    Function which takes an audio directory as input (which should contain a series of wav files), \n",
+    "    and returns true labels, predicted labels, and a dictionary of probabilites corrosponding to labels\n",
+    "    for each file.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    ----------\n",
+    "    audio_dir : location of folder containing wav files\n",
+    "    \n",
+    "    Returns:\n",
+    "    --------\n",
+    "    y_true : A list containing the true labels for each file\n",
+    "    \n",
+    "    y_pred : A list contianing predicted labels for each file\n",
+    "    \n",
+    "    fn_prob : A dict containing the probabilities  of each label for each file\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    y_true = [] # List containing actual labels\n",
+    "    y_pred = [] # List containing predicted labels\n",
+    "    fn_prob = {} # Dict containing probability of given label\n",
+    "    \n",
+    "    print('Extracting features from audio')\n",
+    "    for fn in tqdm(os.listdir(audio_dir)):\n",
+    "        rate, wav = wavfile.read(os.path.join(audio_dir, fn))\n",
+    "        label = fn2class[fn]\n",
+    "        c = classes.index(label) # Grab true label\n",
+    "        y_prob = []\n",
+    "        \n",
+    "        # Step through audio file in chunks\n",
+    "        for i in range(0, wav.shape[0]-config.step, config.step):\n",
+    "            sample = wav[i:i+config.step] # Take chunk of audio file\n",
+    "            x = mfcc(sample, rate, numcep=config.nfeat,\n",
+    "                     nfilt=config.nfilt, nfft = config.nfft) # Take mfcc of our sample\n",
+    "            x = (x - config.min) / (config.max - config.min) # Normalize based on max/min\n",
+    "            \n",
+    "            if config.mode == 'conv':\n",
+    "                x = x.reshape(1, x.shape[0], x.shape[1], 1)\n",
+    "            elif config.mode == 'time':\n",
+    "                x = np.expand_dims(x, axis=0)\n",
+    "            y_hat = model.predict(x)\n",
+    "            y_prob.append(y_hat)\n",
+    "            y_pred.append(np.argmax(y_hat))\n",
+    "            y_true.append(c)\n",
+    "            \n",
+    "        fn_prob[fn] = np.mean(y_prob, axis=0).flatten()\n",
+    "        \n",
+    "    return y_true, y_pred, fn_prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting features from audio\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [01:08<00:00,  4.36it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('data/instruments.csv')\n",
+    "classes = list(np.unique(df.label))\n",
+    "fn2class = dict(zip(df.fname, df.label))\n",
+    "p_path = os.path.join('pickles', 'conv.p')\n",
+    "\n",
+    "with open(p_path, 'rb') as handle:\n",
+    "    config = pickle.load(handle)\n",
+    "    \n",
+    "model = load_model(config.model_path)\n",
+    "\n",
+    "y_true, y_pred, fn_prob = build_predictions('data/clean')\n",
+    "\n",
+    "\n",
+    "acc_score = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
+    "\n",
+    "y_probs = []\n",
+    "# Iterate through each sample\n",
+    "for i, row in df.iterrows():\n",
+    "    y_prob = fn_prob[row.fname] # Grab probabilities for each class for the sample\n",
+    "    y_probs.append(y_prob)\n",
+    "    for c, p in zip(classes, y_prob):\n",
+    "        df.at[i, c] = p # add probabilities to dataframe\n",
+    "    \n",
+    "y_pred = [classes[np.argmax(y)] for y in y_probs]\n",
+    "df['y_pred'] = y_pred\n",
+    "\n",
+    "# Save as csv file which now contains all samples along with true label, predicted label, and probabilities for each class\n",
+    "df.to_csv('predictions.csv', index=False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Audi_Classification_ML/__pycache__/cfg.cpython-37.pyc b/Audi_Classification_ML/__pycache__/cfg.cpython-37.pyc
new file mode 100644
index 0000000..7887f57
Binary files /dev/null and b/Audi_Classification_ML/__pycache__/cfg.cpython-37.pyc differ
diff --git a/Audi_Classification_ML/__pycache__/eda.cpython-37.pyc b/Audi_Classification_ML/__pycache__/eda.cpython-37.pyc
new file mode 100644
index 0000000..0a8c3b6
Binary files /dev/null and b/Audi_Classification_ML/__pycache__/eda.cpython-37.pyc differ
diff --git a/Audi_Classification_ML/cfg.py b/Audi_Classification_ML/cfg.py
new file mode 100644
index 0000000..eaa5189
--- /dev/null
+++ b/Audi_Classification_ML/cfg.py
@@ -0,0 +1,12 @@
+import os
+
+class config:
+    def __init__(self, mode='conv', nfilt=26, nfeat=13, nfft=512, rate=16000):
+        self.mode = mode
+        self.nfilt = nfilt
+        self.nfeat = nfeat
+        self.nfft = nfft
+        self.rate = rate
+        self.step = int(rate/10)
+        self.model_path = os.path.join('models', mode + '.model')
+        self.p_path = os.path.join('pickles', mode + '.p')
\ No newline at end of file
diff --git a/Audi_Classification_ML/cleanup.ipynb b/Audi_Classification_ML/cleanup.ipynb
new file mode 100644
index 0000000..f040378
--- /dev/null
+++ b/Audi_Classification_ML/cleanup.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "from tqdm import tqdm\n",
+    "from matplotlib import pyplot as plt\n",
+    "import eda\n",
+    "from python_speech_features import mfcc, logfbank\n",
+    "from scipy.io import wavfile\n",
+    "import librosa\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            fname      label\n",
+      "0    5388d14d.wav  Saxophone\n",
+      "1    c685f05f.wav  Saxophone\n",
+      "2    36d20ab5.wav  Saxophone\n",
+      "3    d6665734.wav  Saxophone\n",
+      "4    7352e28f.wav  Saxophone\n",
+      "..            ...        ...\n",
+      "295  3c713bcf.wav   Clarinet\n",
+      "296  2fc00271.wav   Clarinet\n",
+      "297  b0c06255.wav   Clarinet\n",
+      "298  71c6451f.wav   Clarinet\n",
+      "299  5de123c3.wav   Clarinet\n",
+      "\n",
+      "[300 rows x 2 columns]\n",
+      "          fname      label\n",
+      "0  5388d14d.wav  Saxophone\n",
+      "1  c685f05f.wav  Saxophone\n",
+      "2  36d20ab5.wav  Saxophone\n",
+      "3  d6665734.wav  Saxophone\n",
+      "4  7352e28f.wav  Saxophone\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join('data', 'instruments.csv'))\n",
+    "print(df)\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.set_index('fname', inplace=True) # Set our index to be the fname collumn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  label\n",
+      "fname                  \n",
+      "5388d14d.wav  Saxophone\n",
+      "c685f05f.wav  Saxophone\n",
+      "36d20ab5.wav  Saxophone\n",
+      "d6665734.wav  Saxophone\n",
+      "7352e28f.wav  Saxophone\n",
+      "...                 ...\n",
+      "3c713bcf.wav   Clarinet\n",
+      "2fc00271.wav   Clarinet\n",
+      "b0c06255.wav   Clarinet\n",
+      "71c6451f.wav   Clarinet\n",
+      "5de123c3.wav   Clarinet\n",
+      "\n",
+      "[300 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the rate and signal for each of our wavfiles\n",
+    "# and add their ratio to our df as a length collumn/feature\n",
+    "for f in df.index:\n",
+    "    rate, signal = wavfile.read('data/wavfiles/'+f)\n",
+    "    df.at[f,'length'] = signal.shape[0] / rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  label  length\n",
+      "fname                          \n",
+      "5388d14d.wav  Saxophone    4.14\n",
+      "c685f05f.wav  Saxophone    1.54\n",
+      "36d20ab5.wav  Saxophone    4.90\n",
+      "d6665734.wav  Saxophone   10.50\n",
+      "7352e28f.wav  Saxophone    6.24\n",
+      "...                 ...     ...\n",
+      "3c713bcf.wav   Clarinet    6.14\n",
+      "2fc00271.wav   Clarinet    4.20\n",
+      "b0c06255.wav   Clarinet    4.08\n",
+      "71c6451f.wav   Clarinet    3.56\n",
+      "5de123c3.wav   Clarinet    3.34\n",
+      "\n",
+      "[300 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a class list and class distribution\n",
+    "classes = list(np.unique(df.label))\n",
+    "class_dist = df.groupby(['label'])['length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label\n",
+      "Acoustic_guitar     6.948667\n",
+      "Bass_drum           1.937333\n",
+      "Cello               5.000667\n",
+      "Clarinet            6.596000\n",
+      "Double_bass         3.206000\n",
+      "Flute               8.054667\n",
+      "Hi-hat              3.357333\n",
+      "Saxophone           7.124000\n",
+      "Snare_drum          3.987333\n",
+      "Violin_or_fiddle    4.530000\n",
+      "Name: length, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(class_dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.set_title ('class_distribution', y = 1.08) # 1.08 to move title up slightly\n",
+    "\n",
+    "# Setup pie chart with autopct to round off floats\n",
+    "ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',\n",
+    "      shadow=False, startangle=90)\n",
+    "\n",
+    "ax.axis('equal') # Make the pie chart a circle rather than elipse\n",
+    "\n",
+    "plt.show()\n",
+    "df.reset_index(inplace=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we have an idea of how the classes are distribution. We can see the bass drum constitues a disproportionately small amount of the data. In order to even out the data we would have to throw out a lot of data which is not ideal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "signals = {}\n",
+    "fft = {}\n",
+    "fbank = {}\n",
+    "mfccs = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_fft(y, rate):\n",
+    "    n = len(y)\n",
+    "    freq = np.fft.rfftfreq(n, d = 1/rate) # d is our spacing between signal (time that passes between each sample)\n",
+    "    Y = abs(np.fft.rfft(y) / n) # Find the magnitude of our fft, scaled by length of signal\n",
+    "    return(Y, freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab a sample for each class then calculate its signal, fft, fbank, and mfcc\n",
+    "for c in classes:\n",
+    "    wav_file = df[df.label==c].iloc[0,0] # Use iloc to select by position\n",
+    "    signal, rate = librosa.load('data/wavfiles/'+wav_file, sr=44100) # Read in signal and rate with sr coming from scipy.io\n",
+    "    signals[c] = signal\n",
+    "    fft[c] = calc_fft(signal, rate)\n",
+    "    \n",
+    "    bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T # Get nfft with sr / 40\n",
+    "    fbank[c] = bank\n",
+    "    mel = mfcc(signal[:rate], rate, numcep=13, nfft = 1103).T\n",
+    "    mfccs[c] = mel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_signals(signals)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fft(fft)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fbank(fbank)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_mfccs(mfccs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at our time series, we can see a lot of empty space which we can remove later. Looking at Filter Banks and MFCCs we can start to tell each apart. We now create a function to find the envelope of a wave, an example of which we see here. This can help by ignoring, for the most part, portions where the values fall to zero.\n",
+    "\n",
+    "![](figures/signal_envelopes.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def envelope(y, rate, threshold):\n",
+    "    mask = []\n",
+    "    y = pd.Series(y).apply(np.abs) # Convert to series and apply absolute value to deal with negative signal portions\n",
+    "    y_mean = y.rolling(window=int(rate/10), min_periods=1, center=True).mean() # Get rolling average\n",
+    "    for mean in y_mean:\n",
+    "        if mean > threshold:\n",
+    "            mask.append(True)\n",
+    "        else:\n",
+    "            mask.append(False)\n",
+    "    return mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "signals = {}\n",
+    "fft = {}\n",
+    "fbank = {}\n",
+    "mfccs = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab a sample for each class then calculate its signal, fft, fbank, and mfcc this time with the mask\n",
+    "for c in classes:\n",
+    "    wav_file = df[df.label==c].iloc[0,0] # Use iloc to select by position\n",
+    "    signal, rate = librosa.load('data/wavfiles/'+wav_file, sr=44100) # Read in signal and rate with sr coming from scipy.io\n",
+    "    mask = envelope(signal, rate, 0.0005)\n",
+    "    signal = signal[mask]\n",
+    "    signals[c] = signal\n",
+    "    fft[c] = calc_fft(signal, rate)\n",
+    "    \n",
+    "    bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T # Get nfft with sr / 40\n",
+    "    fbank[c] = bank\n",
+    "    mel = mfcc(signal[:rate], rate, numcep=13, nfft = 1103).T\n",
+    "    mfccs[c] = mel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_signals(signals)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fft(fft)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_fbank(fbank)\n",
+    "plt.show()\n",
+    "\n",
+    "eda.plot_mfccs(mfccs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can see a lot of the dead space, such as the gaps in flute, has been removed. We can now apply this concept to all of our data and save it in our 'clean' folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [02:10<00:00,  2.30it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if len(os.listdir('data/clean')) == 0:\n",
+    "    for f in tqdm(df.fname):\n",
+    "        signal, rate = librosa.load('data/wavfiles/'+f, sr=16000) # Load in signal and rate\n",
+    "        mask = envelope(signal, rate, 0.0005) # Create envelope\n",
+    "        wavfile.write(filename='data/clean/'+f, rate=rate, data=signal[mask]) # Write in wav file with signal indexed by mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/Audi_Classification_ML/eda.py b/Audi_Classification_ML/eda.py
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--- /dev/null
+++ b/Audi_Classification_ML/eda.py
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+import os
+from tqdm import tqdm
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.io import wavfile
+from python_speech_features import mfcc, logfbank
+
+def plot_signals(signals):
+    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
+                             sharey=True, figsize=(20,5))
+    fig.suptitle('Time Series', size=16)
+    i = 0
+    for x in range(2):
+        for y in range(5):
+            axes[x,y].set_title(list(signals.keys())[i])
+            axes[x,y].plot(list(signals.values())[i])
+            axes[x,y].get_xaxis().set_visible(False)
+            axes[x,y].get_yaxis().set_visible(False)
+            i += 1
+
+def plot_fft(fft):
+    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
+                             sharey=True, figsize=(20,5))
+    fig.suptitle('Fourier Transforms', size=16)
+    i = 0
+    for x in range(2):
+        for y in range(5):
+            data = list(fft.values())[i]
+            Y, freq = data[0], data[1]
+            axes[x,y].set_title(list(fft.keys())[i])
+            axes[x,y].plot(freq, Y)
+            axes[x,y].get_xaxis().set_visible(False)
+            axes[x,y].get_yaxis().set_visible(False)
+            i += 1
+
+def plot_fbank(fbank):
+    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
+                             sharey=True, figsize=(20,5))
+    fig.suptitle('Filter Bank Coefficients', size=16)
+    i = 0
+    for x in range(2):
+        for y in range(5):
+            axes[x,y].set_title(list(fbank.keys())[i])
+            axes[x,y].imshow(list(fbank.values())[i],
+                    cmap='hot', interpolation='nearest')
+            axes[x,y].get_xaxis().set_visible(False)
+            axes[x,y].get_yaxis().set_visible(False)
+            i += 1
+
+def plot_mfccs(mfccs):
+    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
+                             sharey=True, figsize=(20,5))
+    fig.suptitle('Mel Frequency Cepstrum Coefficients', size=16)
+    i = 0
+    for x in range(2):
+        for y in range(5):
+            axes[x,y].set_title(list(mfccs.keys())[i])
+            axes[x,y].imshow(list(mfccs.values())[i],
+                    cmap='hot', interpolation='nearest')
+            axes[x,y].get_xaxis().set_visible(False)
+            axes[x,y].get_yaxis().set_visible(False)
+            i += 1
+
diff --git a/Audi_Classification_ML/figures/Signal_envelopes.png b/Audi_Classification_ML/figures/Signal_envelopes.png
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diff --git a/Audi_Classification_ML/model.ipynb b/Audi_Classification_ML/model.ipynb
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+++ b/Audi_Classification_ML/model.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from scipy.io import wavfile\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM\n",
+    "from keras.layers import Dropout, Dense, TimeDistributed\n",
+    "from keras.models import Sequential\n",
+    "from keras.utils import to_categorical\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from sklearn.utils.class_weight import compute_class_weight\n",
+    "from tqdm import tqdm\n",
+    "from python_speech_features import mfcc\n",
+    "from cfg import config\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/instruments.csv') # Read in our CSV file\n",
+    "df.set_index('fname', inplace=True) # Set the fname collumn as our index\n",
+    "\n",
+    "for f in df.index:\n",
+    "    rate, signal = wavfile.read('data/clean/'+f) # Grab rate and signal from each file\n",
+    "    df.at[f,'length'] = signal.shape[0] / rate # Set the length of each file based on signal and rate\n",
+    "\n",
+    "classes = list(np.unique(df.label)) # Grab classes\n",
+    "class_dist = df.groupby(['label'])['length'].mean() # create a distribution of the classes based on the mean number of each"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zw9OcWG6lw9DBPN3avADcbbzejonxdDsg+VNnXIrl3XZIwe0E3JqrB96YOWtg7fy/hg6A8XQ7EPlTZ2Ri7YK6Kd62tDYd2tNVPfoU3/dmczgXeAz4xZTzt7WpxOqG5mM83Q5C/tQZQ7H2/Xd4we3o3MzfNmVzuAdWcqFHgVkzZw3sHWezDFHCiG4HIH/qjIuxqq7GNd2ioeX01Z3zL+Sjc2odngismjlr4MRojiUiPxORdSKyWkRWikij5X9aExHxiMi98bQhFhjRbefkT51xL/ABkBFvWwwtw6mB3Q/z0PB6TncFPp45a+A3ozGWiJyLVe3jLFU9A6vE0u4W9tkqNRdbq994YUS3nZI/dYY7f+qMF4EnMP/H9o9q6Mf85mgyVfVVkQarbNNLM2cNfCQKI/YEilTVaw2vRaq6T0R2isijIrJcRNaIyKkAIjJGRBaKyAr75yn28ZtF5A0ReR+r0jUicp+ILLU96EcbMsL2tjeJyP+AU8KOzxaRX9mVu+8SkRdFJLwKd7n9c5KIzBGR10Vks4j8RkRuEJEltv0Do/C3iirmw9oOyZ86oztWRV8Tv+0gnMmyeSNYcUYTm3tmzho4beasgS1JUPQx0McWqmdEJDx0UaSqZwHPYuXnAKts/QRVPRN4GPhVWPtzgZtU9XwRuQgYjFWUdAQwUkTqnPAUkZHAdcCZwFXA6FpNslR1oqr+v0Zey3DgLqwiqDcCQ1R1DFZVjzsauTbmtFnRFZGgHWdaZX/rjo3x+OWxHK+p5E+dcSZWna5z422LITq4tWrD3TwR6fv7W8B/Z84amNWcMVW1HBiJlaj+EPCaiNxsn37b/vk5kG8/zwTeEJG1wB+B08K6+0RVa9JWXmQ/VgDLgVOxRLguzgPeUdVKVS0Fptc6/1oTX85SVd1ve+3bsD1uYE2Y/W2GNiu6QJWqjlDV4cBPgV/H2yARiWupmvypM84H5tEGKzkYmolqlYefuV0Em+O1TgYWzZw1sHZR0yYOrUFVna2qjwA/Ar5mn/LaP4NATTz1ceBTVT0duAxICusqvEKGAL+2P7sjVHWQqv69ITMaOBfebwBbr0REsEItNXjDnofCfg+F2d9maMuiG04GcBRARNJEZGZYzOkK+3iqiMywPeO1InKtffw3IrLeji/9vr4BRKS/iCyyY1GPhx2fJCKfisgrwBoRybe/7WvO3ysiHvv5bBH5o4jMFZENIjJaRN4WkS0i8ouW/AHyp874EtaEWWpL+jG0LS7m/aV92TWgBV2cCiyeOWvgqEguEpFTRCTcAx0B7Grgkkygpvz8zQ20+y9wq4ik2eP0EpH6kivNBa4UkWQRSccS8/rYieWZA1xBO8r9XJs29y0QRrKIrMT6Ru0JnG8frwauVNVSEckBFovIdODLwD5VvQRARDJFpCtwJXCqqqqINHQr9n/As6r6TxG5vda5McDpqrpDRPIbsdunqhNE5C7gPaw3yhFgm4j8UVUPN/H1H8PeYfYm4I70WkPbpYseWfZNpkVjg0c34JOZswZeMOX8bZ838Zo04Gn7MxEAtmKFGi6tp/3vgGki8mNgVn2dqurHIjIUWGQ5pJQD3wQO1tF2uYi8BqzEEvx5Ddj7V+A9EVkCzOREL7hd0WZ3pIlIuarWfFueixUUPx3ri+KPwASs24dTgP5Y3vB/gdeBD1R1nr3U5HOsTQMz7OO+esY7DPRQVb+IZGAJeJqITAIeUdXJdrt8u5/T7d/vBdJU1SMis4GfqeoCETkf+KmqXmi3mwvcqaorI/k7rHr4zK9c7XvkNT8J7S7ReGvS3nekiYYOP833gl04Gs0Um0eAKVPO3xbRe8wQW9pFeEFVFwE5QC5WPoFcYKSqjgAOAEmquhnLq1wD/FpEHlbVAJaX+hbwVeCjxoaq53idsSWbpFptw+NJtWNNkd1ZeDIvGe7Y/s5c9z0bEvF7G7/A0F64lee3RllwwVrL+8nMWQNPj3K/hijSLkTXXivoBA5jxZYO2h7pZKCf3SYPqFTVfwG/B86y40qZqvofrAq3IxoYZgHW8hVoOFHMAaCbiGSLiJv6b8dahifzQqwvi8SecmT0XPfdq43wdgzyddu88/mktXZ/5QAzZ84a2GZ2J9qflZV1PLLjbVs8aA8xXbBmRG9S1aCIvAy8LyLLsGJBG+02BcATIhLCSgr9QyAdKw6UZPdxTwPj3QW8Ysdi36qvkS32jwGfATvCxo8enszRwLuExXB7yNHRc913L5vgfbLAR4KJ7bZTnBrY9RAPn9nKw3TD2r02bsr52/a38liNYs9jNOTwdCrabEy30+LJHAAsop5yOgc1a9l53idP95JYO6zRqWiXMV3V4E95dMPprInV7f8aYMKU87cVx2g8QxNoF+GFToMnMxv4kAbql3WT4lHz3Hetc+Orjp1hhmgwmsXzYii4YN39fTBz1sDkGI5paIROJ7r2Xu/asaWfxdsuPJkJWDuBhjTWtJuUjJzvvmtdEt6q1jfMEA2StXLdHfwhHknJx2Ft5zW0ETqd6KrqL8N2y9Q8fhlvu4C/YC2DaxK5lvBuMMLbDlCt8PDTVCeheM2h3DRz1sDvxmlsQy06nei2STyZ9wC3RnpZjpSeZYS37XM5by/vzZ78OJvx9MxZA0c23szQ2hjRjTeezLFYu32aRY6UnrXAfefGZLyVUbTKECWy9dCSa3nlvHjbgbUS5s2ZswZ2ibchnR0juvHEk5mDlUmpRbed2VJ25nz3nZuM8FpUvvUKRbdeTdEtX6PizZdPOl/x72kc/u61HP7utRTdejUHLhhJqLSEUPERjtx5C0W3Xk31/E+PtS9+6G6CRSftYm0U0dChx5jarGQ0rUQ+Vj5eibchnZm2vE63Q1MwrUC+2TXr0fuPFPeKxicgW8rOXOi+Y+U471ODK0mKalKcov88SdW2pThTMsn79jMAHP30BSq3LkGcLlxZPcj5yt04kk7eqbzn2VtxJCaDw4E4nPS86Unr+tn/oGr75yR260/OpT8BoHztLELVZWSMuqLZtgZ2bKVyxttkP/MSJCRQ/MDtuM8Zj6t3v2NtUq+7idTrrFTE3oVzqHjzZRwZmVS+/QpJF11G0vlfoviB20kaPxnvwjm4Bg/FmRP55rHv8syOLIrHNPvFtA6XAA8CbWEeo1NiPN34cee/MjNuu7BP3rIjDkfESXDqoouUj1jgvnNrCtVRTQaSVnAB3b5+YgGApPwR5H37z+Td+icSuvaiZPEb9V7f/fpfkXfL08cEN+StwLt3A3m3/gnVEL5DOwn5vVSs/R/pZ17SIlsDu3aQMKwASUpGnC4Sho/EG+a11qZ61kcknf9l6xenC3zV4PeBw4EGA1S+9Qqp134rYjsG6uZ5E/m0rQluDY/NnDXwgngb0VkxohsHCqYVnAH8FuCAyzV6ct9evtkpyVFJUtJFyocvdN+xNZWqqCVhT+pzOs7kE6vIJPc/C3FY6YXdeacQKCuKoEdBgwFUFQ34EIeT0iVvkz7ycsTZspsvV/+B+FcvJ1RSjFZX4ftsPsGDhXW21eoqvEsXkjRhCgBJUy7Gu3QRRx+4ndSbvk/Ve6+TdNElSFJky1xd6t/xMzxtedLKAbwyc9bAvHgb0hkxohtjCqYVJAGvErbFNyTS845uOQX35mbPDlmJcVpEllQMX+C+c3s0hbchyld/QvKAetK5inDw9YfZ/+JdlK208g053CmknDKW/S/eiSuzO+JOxbd/MymDaxfBjRxXvwGkXnczR+/7IUcfuB3XwCH1Crl30VwSThuBIyPTsistnS6/fprsv7xCwuCheBfNwz3hAkp//xjFnnvxrVvVuAGqgQd4vMqNN6XFL6Z1ycVapmiIMUZ0Y8/PgWEnHRVx/jctddLEvr1WFzqddbtmEZAlFWcsdN+xPZWqspb21RAlC18Dh5PUYZPqPN/jht/R8+b/o9vXH6Vs+QdU77byv2eefTV5tzxN1/O/Q8m8f5F13jcpW/VfDr37G4oX/rtFNiV/5Uqyn3+Vrv/3ApKeibN33zrbVc/6L0lTvlznuYp/Pk/qN79N9cyPcA0ZSsZ9Hsr//qdGxz6X+QuGse7k/2/b5LKZswZeG28jOhtGdGNIwbSCUzle6K9Oip3OERf1yXN9kJqyrKXjZUrlGQvdd+xsLeEtXzOTym1LyLnsXuyE1SfhSrcSSTlTs0gZci7efZtPOO87sM1q16UXFWtnkfvVqfgP7cJ/ZO9JfTWV0FGrXFfwwH6882Ydj9mGtykvw7f6c5LGTjrpXGDPLoKHD5E4fBR4q8HhABHwNZzkLUUr1tzGU/HYddYSnpo5a2DXeBvRmTCiG1ue4cTaTnWiIjk/zc0e+b3uuXP8Vsa0ZpMplQWL3HfsTKOytCX91KZq++eUfvYm3b72MI6EunPvhHzVhOxVbCFfNdU7VpCY2++ENsXz/kXm+BsgFAC1IyviQAPNz2JZ7LmXoluuovhnd5F+11Qc6RlUTn+DyunHJ/u88z8lcdQ5SPLJ8dryv/+ZtFtvAyDp/C9T/dF0jtz+LVKuaWBCTbX8MaZmOgjFtY5eM+gWCkmz14kbIsdkGYsRBdMKbgD+Fel1qaHQutf2Fmb0CwRaVIyyVJPXjvU+3beclIxIrz00/Xd4v1hDsKoUZ0oWmeNvoHTxG2jQj8OeYHPnnUL2l35EoOwwhz96iu5ffxR/cSGH3rZLw4VCpA6bSObY43ezlZsX4Tu4g6zx3wDg6Ky/U7VjOQnd8sm97L4GbWprWcau0tfmfY3X28ImiIioqkxftHr1hf19vtTrPB7PnHjb0xkwohsDCqYVZAKbgO7N6kC1ZOqRo+tvKC1vUdn1Mk1eN9b7VO8yUjNb0k9boC2Jbq4eWPwkt7V8FjCGhEKye/PmsQcOHTw2A7oGOMvj8QTiaVdnwIQXYsMvaa7gAohk/ia767nf6Nl9rldodkrHdKk6baH7zj3pVJQ02xbDCYiGDjzGA41mhmsrqBI4crjXnEULr8sJE1yw0kDWLshqaAWMp9vKFEwrGAksIUpfcO5QaMu/9h9wnOrzD2xuH+WatH6s96lepaS1W4+3rXi6t+mTy8YxL6Ly5/EiEEhYs2b1hUnl5dmD62lSAgzxeDyR73k2NBnj6bY+fySKf2evwzH463k9ej6blTG/uX2kSfWwhe4792ZQbjzeFjBEN8xtD4KrSvHuL06ft2jhtac3ILhg1R/8bazs6qwYT7cVKZhWMAX4X2v1P9DnX/CvfYVnpKmmN976ZCo0acNY71M9S0jLirZtrU28Pd0E9W17jpvy3PjadFWGiorMBWtWXzjE70/ObeIlCgzzeDzRr/1nAIyn29o80pqdb0tMGDehX+/Dy92JG5pzfapUD13ovmN/FmWmhlYkqPofxONvy4IbCjl2bVg/Yfnyzy8fF4HgglXAteGlI4YWYUS3lSiYVjAZaPUlRH6R/Jt6dh/4q+wuzVrukyreoQvddxZmUXY02rZ1VM5j9sK88vWnPuo5wC037+bWW3azfl3d85sbN1Zz0YXbmTvH2pG9e7ePH/5gD9/77p5j1wSDyn337ae6usU7wFHFV1TUZ87Chdd2Lyrqd1Yzu/mmx+Pp1WJjDHViRLf1aFUv9wREEl/NSJ94UZ+8JcUOR8TimSLeUxe67zzYhdIjrWFeRyJVy1Z9jz+f9+c/HWb06GT+8WIfnnu+N337JZzUNhhU/vbXI4waddwh/uD9Mr7zna48/Eh3Xn/DusGYPr2UCy9IIympZR9Hvz9x1Yrll+7ZsH7SRA25WlItOhG4u0XGGOrFiG4rUDCtYBIwMdbj7ne5xkzq26tqXnLS6t0lISZPq2Don8s57Zly/m/xyTu83tvo54xnyxnxl3Im/PXwKY/v/UF5F0qP+A/vYf+Ld7HvhR/h3WtFLjQU5MC/f0bI34mLEKuWPsbU7KqKoGPNmmou/ooVSk9IENLSTt6I9u67pZx3XipZWcfPuVzg9SlebwiXUygvD7J4USUXXnRyLuKmm8WRXTvPmL940TVnVFR0GdDsjk7k+x6Pp92ubmnLmCTmrYMnXgMHRfJu657bfZy3ZOETF+o5o/KcjjKvMvL5Ci4c6GJY7nEBmDLAxeWnuBARVh8Ics0b5X1X3H7n5s/iDB4AACAASURBVL7Lz0rJmnhzkiuzG8VzppF75VDKVvyH1NPOr3fLb2fgGl5Z04PCcVv3+8nMdPLE7w6xbbuPIYPd3HZ7NsnJx32YokMBFsyv4Inf92TTpkPHjl9+RSa//e1B/D7lnntyeOmfxXzjhqx6c1c0Rnl5l/lr11ww1O9PinbOh3Tgh8Bvotxvp8d4ulGmYFrBBOLg5Z6AiHNBv6yxPzmn78qDTufBdLcwNNfB3tITV6qkJcqxD3uFTxGBZPENudo5tyrVV1SqAS84nISqy6nauoTU08+Px6tpE3TX/Yuu4O1xAMEgbNni5bLLM3juud4kJQn//veJc5HPPHOY73y3K07niWLavbuLP/whj6f/1At3koPDhwP07ZPAb359kMcfP8Ce3b4m2RMKOXasWzdp5Yrll473+5Oyo/Qya3OXx+NxN97MEAnG040+d8bbgBqOOJ1nXdAn79Ad6w+sWbG/rODs3iffAr+zwc9PZ3o5WBFixjesFLA/HefqsvyNP1etDfYNZF58j6t4watknntNs72x9o5Dg/sf5aen1vyem+skN9fF0KGW1z9hQiqv1hLdzZu9/PIX1h6DkpIgS5ZU4nQK48Yfr6T0wgtHuOWWrrzzTinnT0mjRw8X/3ypmAcfrL80kCrVhw7lL968aexYVWdr11/rAdwEPN/K43QqjKcbRQqmFXQDLo+3HeEEvKHcO18vLxh4Wc7aFLectK/+yqEJbPxRGu9el8LPP7Xivn0zHXz2nZTkw98r2dnVWX0kWH6EhOzeFH3w/zj03m9blHax3aGqP+KP+9MpO1ZFt2tXF7m5LnbbXunyFVX063di8rh/vdyXl1+xHhMmpHLnnTknCO6qVVXkZLvo3TuBam8IhwMcDsHbwAoGny9p+fLPLyvctPG8SarORrPVRYl7PR6P0YkoYjzd6HIzcPI0dpzQgLL7T7vJOjeLogtzTj8vGFrz+r7CLn0Cgd61207o52Lb0SqKKkPkpFifsSTxD8qf+2C5TPjFkS8+f79r6rBJuDK7UbzglUazgHUUhrF27tksOilc9KM7svn1rw7i90PPni7uuz+X99+3smdedlnDidxUlZf/VczPH7Y82ksuyeDXvzpIMAh33Z1TR3sO7dxx5uY9e04fF43XFCGDgSuBt+IwdofE7EiLIgXTCjZjvUnjjqqy9697caY66XlDz2PHRbX4ocNHN15TVn7O1iMhBnax4rrL9we57NVK9tyTdiyMMGdngPc2Bfj1Renb+r4/oieDJ6a4MrtzdPY/6HbVQ/F6aUBsdqQlqHfL89zUJxF/XGYPVdHy8uz5a9dMKQgE3PHcNbjU4/G01SKb7Q4julHCXiZWf9nZGFOxuYIdv9qBu7f7mIh2v7o7/sNWTvQpY1PmDnl999hX1/hdCQ5IThCeuNDN+L7WzY+qctG/Knn96hS6JAvLCtk94e3MngGcrq4X3UZS7/hWpGl10VX1PcbUnQPZGpcMYsGgc8uG9ROrjh7tdUY8xq+DMzwez5p4G9ERaPfhBRHpATwJjAa8wE7gblXdXE/7clVNE5F84ANVPT1Kpnw3Sv1EhdQhqZz+Yv0vbQVM2PDNgZve23cgcZDff9KEjIjwyY3HY5CjetDn6A8rdkz0PplSSNfmp6lsJ0xi5qKBbI35KhRVqg4eGPDZli3njlN1tJlQFXAVVs5dQwtp1wFysVy4d4DZqjpQVYcBD9KS3LXNoGBaQVfga7EcMxpUOxynXNmrR+7zmU3LWOaWQP857rsre3DkQGvbFk/StWTFt/lLzKtAeL3Jy5Ytu6Jo8+Zxk9qY4EI7fH+3Vdq16AKTAb+qHislraorVXWeiNwnIktFZLWIPNpQJyKSJCL/EJE1IrJCRCZHaMcNhJVUb1eIpD3dNWv8Vb16zK8UqWisuVsC/ee6767syeEWVyxuk6iWPM4D3R1ozD4bqnJg29ZRi5Z8dvWo6qqMFpVlakUKPB7PoHgb0RFo76J7OvB57YMichHWhNYYYAQwUkQaiv/dDqCqBcD1wDQRiWTy5JoI2rZJtiQmjh/fr/fB1e7ETY21TZRA/znue6rzKNofC9tiyfX8c10uh/JiMZYqodKS3LmLFl6TtG/f0BaVYooRxtuNAu1ddOvjIvuxAlgOnErDqwrGAy8BqOpGYBfQpAmUgmkFucDYlhjbVvCL9L+hZ/f8J7pmzW2sbaIE8me77/H14lCHEd483bPwUqbH5H8ZDDo3rVl94YZVq748IRhMbC85Dq6KtwEdgfYuuuuAkXUcF+DXqjrCfgxS1b830E9LtlpdTvv/Ox5HxP3PzIwJF/fOW1zikAYrSyRKsN9s9499veXQvliZ11o4NLjPw4OntfY4qlTs3z94zsIF1w0sKenR6uNFmdEej6ethj/aDe1dLGYBbhE5tnJAREYDpcCtIpJmH+slIvXvrYS5WHFZRGQI0Berem9T+GpzDG/r7ElwnTOxb++yhUlJDc5YJ0iw36eJPw60a+FVDd3NEwdTqWhVj7O6OmXJsqVfLd665ZyJ4GiPK4cEa6OEoQW0a9FVa5HxlcCFIrJNRNZhZfh6xX4sEpE1wJtYWZPq4xnAabd9DbhZVU/OhViLgmkFSUCHzQITFOn9/R65Qx/K6TpHrTIudZIgwb6fJv440EcOtsv9wQWsmjeSpSNaq39V2b9ly9mLly752pjq6vT2nhzcxHVbiNkc0QIKphVcBPw33nbEgpxA8PM39u3vmxMM1Vv6xa/O3VN8v5cvtPtJ24yjTbQ2RySqd/Nf+Va+i0DUcxmoEiwp6b5g/brJZwWDCc1PmNu2CAE9TcXg5tOuPd02wJfjbUCsKHI5R07p00s/SUleXl+bBAn2mZl4L/2ksH14vKrVP+chaQ3BDQZdG1av+tLmNasvmtCBBBcszeiQIbVYYUS3ZXwp3gbEkpBItx93yxlxZ7ec2UEI1tUmQYK9Zybeq/2kcE+s7YuUC/joswFsj2quDFXK9u49Zc7CBdcOKS3tNjSafbchLo23Ae0ZI7rNxN6FFt8EBPFAxPFpasqkCX17rdvrctY5eeaSUO+ZifdKvuzfHWvzmkqGFi+/mb9FNXdDVVXa4qVLrirfvm3MRHCcnLy441DXiiFDEzGi23xGxduAeFLqdJ5xce+85LfTUpfUdd4loV7/S7zP0V/2fRFr2xpF9ejjPNBTWrZU8BihkOzZtOncJcuWXnmO15vas/Er2j15Ho+nodVAhgYwott8OrXoAqhIl0dys8fc0qPbHB+cVGfGJaFenyTe7xog+3bFw776+BYvbMyhqMXiqErg6JGecxYtvLbLwQODOlvqwzPjbUB7pT2uFWwrjG7tAfb8fQ9lK8twZbgY/Esr9FiypISD7x7Eu9/LwIcHktw/uc5ry1aXsf+V/RCCLhO6kHuptehg9192U72nmvQR6fS4ugcAB987SFKfJDLOajj5dn0sS06aOL5f7w3/3leYMsAf6Bd+ziWhvI8T79//Jd9vd23TXv3q6yNW9NZdC77Ef1qcDDwQSFi7Zs0FieVlOfGthxc/zqSTrNyJNsbTbT6t7ul2Gd+F/J/kn3DM3dtN3zv6kjIkpd7rNKTse2kf+T/OZ9CvBlHyWQnVe6up3m2VTx/8i8FUbq4kWBnEX+ynantVswW3hiqHY+gVvXp2fSEzfUHtcy4J9fw48YHEgbI3rh6vQwN7HuGhgpb0oUrJnt3D5i5aeO2w8rKcuOTabSMYT7eZGNFtBgXTCnoArb4WNfWUVJypJ87HJOUl4e7ZcEKzqu1VuLu7SeyWiMPlIPPsTMpWlIET1K9oSNGAggMOvn2QbldFKTwnkv7Hrl3GfT2vx/wqkcrwU04J9fw48X73YNmzMzqDRYhq6Mf89nAKlc3+dqmszFi45LOveXfsGDkBpLN/dozoNpPO/sZpLm06nus/6ieh6/F0rK4uLvxH/STlJZHQNYFtj2wjc3QmvgNWGDa5X90hiuay0Z04fnzf3vvXJiZuCT/uFO3xUeIDSUNk946oDtgEzuTzeWeyfHhzrg2F5IuNG8Yv+3zZFWN9vhQzgWQxyOPxNLTL01APRnSbR5sW3fo37ELPG3oy6PFB5FycY3m5V3bj4PSDfPHnLzgy+0jUTPA5ZOD1ed37/LFL5gkZy5yiPT5MnJpyinwRM+F1a9WGu/ldxNnDVPEfPtx7zqKF1+UeOtS/bf/PY48AzfoS6+wY0W0eLYoLtjYJXRPwH/Ef+z1wNEBClxMLEZQuLyW5fzIhbwjvXi99b+9L8cJiQt76S4BHjEjSC1mZEy7p3XNRaVjGMqdo9/8k/jTlVPlie/QGqwfVqkd4KNFFMKJKDH5/4uoVK76ya/26yRNDIVd0bwU6DibE0AyM6DaPuM/CN0Ry/2S8B7z4DvkIBUKUfFZC+pnH7wQ1oBz+5DA5F+cQ8oWOr1ZV61y0+SIh4dyJfXuXLklyr6s55hTtPiPxp2lDZde2qA8Yxpf5YGk/dg5santVjn6xq2D+4kXXFFSUZ5tKCQ1jRLcZGNFtHn1jMcjuZ3ez/Rfb8RZ62XjPRo7MOULp56VsvGcjVduq2PnHnez8/U7AiuPu/IP1XJxC3jfz2Pn7nWz56RYyRmeQ1Ot4IYzDMw+TNS4Lh9tBUp8kUNjy0BZSBqecNHEXLQIifb7do9sQT/bxjGVO0W4fJD6YMUx2torwZumRZTfyYpN3nVVUZC34bPHVwV27RowHicrGiQ6OEd1mYLKMRYidzrEq3na0Z7oFAkvf2Fs4oGsolA0QUjl0me8XJeu0f5M9y8ayjImGDj/F9wNdOdJokdJQyLFz48bxRw4X9TurqeMbAPB6PJ5IyloZMJ5uczCZ81vIQZdr9OS+vfyzUpJXAjhEc99PfCjzNNmxNVpj3MJftzQmuKp4Dx3qO3vhgut6GsFtFm6Px9NeSg21GYzoRk5MQgsdnZBIj7u65ZzxYztjmS28WQWyfUvjVzdMP90+fwofn9NQG7/fvXL555fu27hh4iRVZ/us5Nw2iGgJnYj8TETW2VW6V4rI2a1lWNiYL4rI1a09TlMxohs5RnSjhYjjk9SUSRP79lqz3+nc7xDNeS/x512Hy9bNze3SqYEvfs7P660CocrhnTuHL1i86JoRlZVd+jd3HMMxGg3f1CAi52KlhTxLVc8ALgDabCa61sKIbuSY8EKUKXE6R3ypT17ie2mpSx2i2e8kPpLdLOFVDd7HL0uSqT4pabgqWl7Wdd7iRV+X3V+c0eLcC4ZjROLp9gSKakphqWqRqu4TkYdFZKmIrBWR58XCZR+bBCAivxaRX9rPp4jIChFZIyIviIjbPr5TRH4rIkvsR/gcwQQRWSgi22u8XnucJ+xx14jItfbxSSIyW0TeFJGNIvKyiDWxKiIjRWSOiHwuIv8VkYgTJ5mEN5HT6tt/OyMqkv1QTteu76elznmm8ODYdxIfkat8j25aqYNOaWofo1gyv4DVJyWgCYUc2zasn1h+5Ejv86JrdWS89957bN68mdTUVG677TYAZs+ezfLly0lJsXJpTJkyhcGDT86rXl1dzfTp0zl48CAiwuWXX06fPn345JNP2Lp1Kz169ODKK62akatWraKqqopzzmkwwhItmuzpAh8DD4vIZuB/wGuqOgf4k6o+BiAiLwGXqur7InIz8KaI3IlVpeVsEUkCXgSmqOpmEfkn8EPgSXuMUlUdIyLfso/VJFzvCYwHTgWmY9VNvAoYgbXJIwdYKiI1m3nOBE4D9gELgHEi8hnwNHCFqh6yRfqXwK0R/A2M6DYDs/WxtRCRz5KTJp7Xr/f61/YWpr3Nw92+5nt00wod3KjwJmnl+jv5/QkerCrVhw72X7x587ljVZ1RL8kTKSNGjGDMmDG88847Jxw/55xzGDu24Q1zH330EYMGDeKaa64hGAzi9/uprq5mz549/PCHP+Ttt9/mwIEDdO3alVWrVnHDDTe05ksJJ7upDVW1XERGAucBk4HXRGQqUCYi9wMpQFdgHfC+qq6zRfh94FxV9YnIcGCHqtbcCU0Dbue46L4a9vOPYcO/q6ohYL2I1HxRjAdeVdUgcEBE5mBlDywFlqjqHgARWQnkA8XA6cAntuPrBPY39fXXYEQ3cswSmVam0uEYdlnvnqX3Hyle+1bJI0Ov9nk2Ltchp9Z7gWqlhweTnYSOvZ99vqTPV6+6KKeqKnNSLGxuCv369aO4uDji67xeL7t27eKKK64AwOl04nQ68Xq9BINBVBW/34/T6WThwoWMGTMGpzNmhSsiqv9mC9xsYLZdffv7wBnAKFXdLSIeTvyMFWCJXY1QNrZ+Wut5Hl7dW2r9rIvw9kEsrRRgnaqe24gNDWJiupFjZrpjgUjG77K7jP1Gr+5rX3I/ljFKNm2or+mlvPt5H3b3B1Dl0PbtZy38bPHXR1ZVZbbpnYM1LFmyhGeffZb33nuPqqqTl4AfPXqUlJQU3nvvPZ577jmmT5+Oz+fD7XYzdOhQnnvuObKysnC73ezbt49TT63/+6kVSG1qQxE5RUTCYycjgE328yIRSQOuDmt/FZYnPQF4SkSygI1Afli89kZgTlif14b9XNSISXOBa0XEKSK59jh1VkKx2QTk2hOCiEiCiJzWyBgnYTzdyDGebgxZ53afN7Ff3pa/7/ut7xfl929YqqeeUOyxqxYtuZ5/naeKlpXlzFu39vzhgYA74uQ28WLUqFFMmDABEWHWrFl8/PHHxzzaGkKhEPv37+fiiy+md+/efPjhh8yfP5/zzz+fcePGMW6cFVWZPn06kyZNYvny5Wzbto3u3bszYUJUy8DVRSSebhrwtC2eAWAr8D0sT3YNsBNYCiAiOcBvsGK3u0XkT8D/qepNInIL8IaIuOz2fwkbw23HXh3A9Y3Y8w5wLrAKyyu+X1ULRaTOby07vHE11hdAJpZ+PokVDmkyRnQjx3i6McbrcAz+Zq/cyhtL/rxICm/TJTp0GIBo6NDjPNA/GHRuWb9uUnVxcV6rK0y0SUs7rlkjR47klVdeOalNRkYGGRkZ9O5tzeEOGzaMBQtOzBW/f78VWszOzuajjz7illtu4c033+Tw4cNkZzc57Nocmiy6qvo5UNcX4kP2ozbHksSr6lNhz2dS/xbkP6vqo7XGvbnW72n2TwXusx/h52djhUBqfv9R2POVWB5xszHhhcgxohsPRFJeykqdUjLgH0dOcW5eB3CrPre5ujB73cIF1/UvLs5r05nf6qOsrOzY8w0bNtCt28krsNLS0sjMzKSoqAiAHTt2kJOTc0KbTz/9lMmTJxMKhajZ2i8i+P3+k/qLMhHFdA3G020OJrwQBxwhDaZUUxqqlvxuiS/svKiwanf65tSJW6rHtpslfG+99RY7d+6ksrKSP/zhD0yaNIldu3ZRWFgIQFZWFpdeaq1wKisrY/r06cdWIVx88cW8/fbbBINBunTpckIIYuPGjeTl5ZGebi2s6d27N88++yzdu3enR48erf2y2oyGqGp+vG1oCibhTYQUTCvYApiUfxHgCqovtZrStCoq0quozKjU6owKvJmVBDMqNZBehaZXImnV6kjx4krykej2k+QKkuQMkepQ0iVswuZo5qD1KwZklQ3pd1H5SveejAq8o5DolFM3RMzbHo/na/E2oj3RZr6l2hGd6sOd6NfKtCrK0qqpSK/UqowqqjMq8GVWaiCjklBGJZpehaRWqzPZZwlmYoAkV4AUp5IqSoZYIZkc+9EivAnph1YUfMdVXfbXUVt3f3jw2t7fDx11VOyYn7Bx7yEpHY2YO5EYUxpvA9obRnQjp7LxJm0AVU32UZ5aTXl6lSWYmZV4MyrxZVZoML2KYEYlpFWppHpxJXtJSPKTmBAg2RUk2REiTSBDrAXr9ZcejiEhcfg/G/PzfYHg1ipgSGWgtOfHe/+x40u9bs28wjd6QBW+ooUJmz7b4Th4GtJygTc0CSO6EWJEN3IqWnsAR0iDKV7KUqsswcyoUm9GJdUZlfgzKzSYUUkovQrSqqzb8WQfCW4/blswUxxKGpAu1u65DrODbsXwuxYFElInBEvXzas5VuIv6v9p4avrJ/e43p0siTlT/AUTAwSrV7h2zFvj/KJ3SNQktWldShpvYgjHiG7kNCi6dvyyLLWa8vRKO35ZiS/TFsz0KkivgtRqdaRUW4KZGCApIUCyHb9ME2tGOMt+GIAd/b48vyRr0AQADR48YdPDoerdwxYdmv75ubmXF4hIogtn0ujAoPNGBQbqZuf+pUtcWxO84q8385ihRRhPN0KM6EbIPW8H1/YoVneKF5fbR6I7QJIrSLIzRJpYEz5JWLtoWnVxZGfiaOag9TvyLx0FEAoe+QJCJ6XX3F2xcWSSM3XRmV2nnC0iDgBB5JRg3uhTgnkUSvGGeQkbjpZI5RjEvO+jiBHdCDFvvgg5d5OmYyXKMMQAb0L6oRUj7uyClV2KoHfdTurJabyl9PNzU10Zc0/JHHPS4vUemjX0675zKZWqvfNdG7bucxw9C+k4oZc4YsILEWI2R0ROUbwN6CzUTJwhzmM5S0P+zQ1mC1t55NMJuys2zq7vfIYm9/qK/6yJN3onhE4J5M0RjTxLlOEEjKcbIUZ0I+dwvA3oLNgTZ8NrflfVkIZKGs3msvDge5OKqvfMbaiNm4TM8wJDJ97inZw7xj9ogUud9SbUMTSIEd0IMaIbOUZ0Y0D4xFkNocDuDTRxcnHm/pfHl/mPNJZlCgcO1xnBfuNu9k4aeoGvYEWqupeimB1DTceEFyLEiG7kHIy3AR2d8ImzcILetYci6Mbx0Z6/j6wOVixv6gX5oW5nXu8dP/pK35jtOaH0eegJOVUNdWM83Qgxohs5La5Wa6if2hNn4YQCO7tE0leIUOKM3c8N8Ye86yO5LlvTB37VN+a8b3jHl+YHc2ej5u6mHkJAJF+EBozoNoetgC/eRnREQuII1J44q0HVV45WD4u0z4D602bsfr57UAM7Ir02BXfuBf4zJt3snZRyRqDfXIdKxH10cLZ5PJ6Ts64bGsSIboQM3bghgPF2W4WVw+9cED5xFk7It2UDkNCcfr2hyuwP9/wtMaShZq1UcOFMHhMYNOEW7+R+4/2nfuZW16rm9NMBWR1vA9ojRnSbR0S3q4bG2dHvy/OLswafVMm3hqBvXYtyXlQESnp9sm9apaoebW4fgjhODfY6+0bvxOGXeM9anxFKXoQSbIld7Rwjus3AiG7ziKg8h6Fh6ps4CycU2N/ivLnFvoMD5xS+tldVW5y0qKd2GXaNb+y5X/edu79nsMsclPKW9tkOMaLbDIzoNg/j6UaJhibOatBQ6X4IDozGeAeqd52++NAH61U1KiUVMjWl9yXWZovgkEDPzrbZwohuMzCi2zyM6EaBkDgCS8Y8tLeuibNwgt5126I57hcV60etOjp7iUYxg7+bhMwJgWETb/FOzhntH7jApY5NjV/VrikDzMRiMzCi2zw2Y1UzNbSAlcPvXOBPSGs0+1fQtynqieM3lSwZt6X08wZ3rTUHB46E4cH8cTd7J58yxVewIkUTl3XQzRZrPR5PR3xdrY4R3WYwdOMGP9bSMUMzaWzirAZVVQ0dGdJYu+aw4sjMiXsrtsxpjb4B+oe6nfkN73mjvuobvS274222MKGFZmJEt/msjbcB7ZWmTJzVoMHCzUBua9ky/+DbEw97989rvGXzydGMQVfamy36WZstjrTmeDHCiG4zMaLbfFr1g9pRacrEWThB75rC1rZp5r6Xxpb7ixe39jgpuHMv9J8x6SbvpKSCQN+5DpVdrT1mK2JEt5kY0W0+H8fbgPZGUyfOwgn6t6e1pk0Aijo/3PO3M6uDlStaeyyABJwpZwcGT7jFO7nPOP8pnyWqqz0KWHu0uU1gSrC3gA2nDt1FPQm1W4Of7d/PnIpyujqdTO8/AICnig4xq6wcEch2OvlVz550c524cWuv389de/cQBAKq3NClC9dldcEXCvGjvXspDPi5PqsL13exUhs8Urifa7O6MCwpuoV1l4+4e05T4rg1qAaqvcVPAbGp8OuSxLLL+96+J8GRODQW44Wzz3Fk3XzXxtJSqRqD4Iz1+BGy2uPx1Llz0NA4xtNtGTH1dq/MzOT53n1OOHZrl668278/7+T3Z2JaGs8UnZybJdfl4pW+/Xgnvz//7pfP3w4f5mDAz/zKCoYlJfFufn9eLykGYGN1NSGIuuA2deIsnJB/+3piJLgAAfWl/2fP8zlBDe6M1Zg15IW6nmZvttjXI5TV1jdbvBtvA9ozRnRbRkxFd1RKCpnOE/9lac7jTlFVSKlrbVWiCIkO6zq/KiH7uAvBq6ET1r49XVTEHTnRrV4eycRZOEHv2pinDawOVuR+tOdvLtXQgViPDZCpKX0u9Y2ceKN3QnCwtdmi1WPazeC9eBvQnjGi2zL+B8c0LG48eegQ52/bygelJfUK5n6/n6/u2MH527byna7ZdHMlMDY1laJAkOt27eTbXbsyq7yMYUlJJ4UnWoI9cZbV1ImzcEKBPU2O/UaT8kBx70/2vVSqqnFL0O0mIXOitdkie5R/wHynOjbHy5Za7PZ4PE3OUWw4GSO6LWDoxg1HgWXxtuPu3FxmDRzEpRmZvFxcdz6XngkJvNu/Px8NGMB7pSUUBQK4RHgiL4+38/vzpfQMXjp6lJu7duW3Bw9w9969zCova5FdYRNneZFeq6GKQxBolfW5TeGor3Dw3ANv7lLVuKYudOBIGBHsP/4W7+Qh5/tO/zxZE+P9fjNebgsxotty2swqhksyMvikrGGh7OZKYGCim8+rTsz58u/io1yRkcmqqioSRPh/eXk8d7hlububuuOsLoK+DVugzmhJzCis2n7GkqL/rFHVNrH7cECo+8gbvOeNusI7ekvXUNp8NC55nY3othAjui3nv/EcfKfv+Ofu0/IyBiS6T2pT6PdTHbKiICXBICuqKumfUv9fKwAAG1dJREFUeLyobkkwyOzycq7IyKAqFEIQBPCGmr+ypTkTZ+EEfRviHrYB2Fm+dsyao/MWRzNPQ0vJ1YzBV/nOHn+9d9zRvsGc2SjNTlcZIcVAq+3g6yy44m1AB2Ax1puxSQUTW8K9+/aypLKS4mCQydu28qPsHOZWlLPD58OBkJfg4pHuPQBYW13Fa8XFPN6jJ9t9Pn538CAioAq3dM1miPt4iPXZw0X8IDsHEWF8aiqvFh/lip07uDareS+puRNn4WiwKCpZxaLBhpJF41NdGXMGZoxo8EukpLqM+z/8HZuKdiDA778ylZG9Tj92ftEXK/j2Ww/SJ8sKVV88ZAJ3j7uZw5XFfPftn1HiLee+877Dl4ecB8Ctb/2UX130E3qk1x2nTyWp+0X+4d39BCo+d22fu965Jz8k2ppLGP/j8Xiikp2tM2PW6UaBDacOfRb4QbztaAt4E9IPLRj7S39z4rg1hAKHtvvKXhoQTbuiwYTuV8/umTJwUn3n75nxS8b0Hs71wy/FF/RT5a8mMyn92PlFX6zguSX/5sWrf3vCdS8se5Mkl5vLh07hxjfu5Z1vPsMnWxewtnAz94y/pcn2KRra4Ny7ZJlrW6pPAgWRv8JGudbj8bzeCv12Kkx4ITr8Pd4GtAVaMnEWTtC3dk+0bIomcw+8Oemot7DO7d9l3go+272K6864BIBEZ8IJgtsQLqeL6oAXX9CHiINAKMDfl73BD86+PiL7BHEMC/Y+51veiQVf9o1Ykx5KXoxGbXWND/gwSn11aoynGyU2nDp0BdCsSaOOQqQ7zuqjuvj5JWj5mGjYFG0ECV7S+/vLUhMyzw4/vu7AFh747xMMzs5nw8FtFPQYwqNT7iQlMflYm0VfrOB77/ycnum5dE/L4aHJt3FKbn9KveXcMf0xDlUe5cGJP2Bz0Q7S3al8veDiFttbLBW75iVs2HlASkYhpLagq488Hk/LDTIYTzeKdGpvt6UTZzWoBv1oecRVf2OFos7/7P3rcG+w6oTilIFQkLWFW/jWmV/lo1v+TkpCEn9e/PIJ157efQiLf/g6H9/6D24ZeRXfeedB/n97dx4fdX0mcPzzJIRDwqEcggSLIsdIFORqpQbQWq2U1traslutxWsbq627jbi2ay3VtUaKXbsaBPFs0ZrVYtWIojVyoygQBTITBDkSCCYQEhJI5nz2j99vYAJIrpn5zSTft695mfzmdzzhePjO93i+AD27pPP8D+ew5KcLyRwwnH9uX8O0EVO4+605/OzV37J+T+sL2vXW7l/5jm/8lOu9Wf7zggOWidLaRR+LWx2E0YhJutGzCGhwOggnRGPgLCwU2FUMxLzITVuENNj1zbIFQwIh/9HdIQb26MfAHv246Czr34tpI6ay+YvG6xl6dOlO986nAXDZ0IsJBINUHaludM6jq5/jFxffwGvF73HBgBHMnXYPD69Y2OaYu9K591T/qKkzvZeePtZabNGSHa1rgBfbHIQBmKQbNS6Puxr4u9NxxFtbVpydTNC7OV7Tn9rEH/L2erPsydNDGtwN0D+9DwN79mf7gd0ArN61nmF9hzS6pqLuAOHuvI17iwlpiNO79Tr6/o6qUr6o28/FZ4+hPtBAiliT97yB6E3HTSWl81hrscWwS32j1nfTzuubcdmzs2fPPhy1IDo4M2Usup4CrnM6iHiJGDiLWl92KLA7uoUfYqghWNf/7T3P7Lpq0M2VIin9Hrj8Tn5R8AD+oJ+ze5/FI9N+zV83WmsJfnLR1SwpWcZfN75GakoqXTt1Ie+7v0Pk2PqPOSue4u7JtwBwtetybln8G575+BVysm6KSfxDQwPGDfUOoEJqtq5M81QelLqJCMevAVcgLyYBdFBmIC2K3CNdgrV/2nlOxxIP0Ro4C9NQQ423Zl46JHxpw0b6dDmr5BsDrx8gIr2aPjtx1dGwb3Wax1OacuAihPDPsmT27NnfdjSwdsZ0L0SRy+NW4Bmn44iHaA2cRQr6S9wkWcIFOODdO2JVxeIdqprUffrpdB1wpX/M1J96p6SeH8hYLiqlwGNOx9XemKQbfU8DjhZJibVoDpxFCnqLnaglEBV7j2wb8/GBpZ+oatDpWNoqjU7pkwIjptzovbQah5e5t0cm6UaZy+OuABY4HUesRHvgLJIGv/hKtO8ZT5/XfvLV4uo1a5yOI1pSkLlmm/XoM0k3NubQDqePRWvF2UnvHTxYCqGkTroAm6tXZe2o3bTM6TiiYDdmmlhMmKQbAy6Puxx40uk4oq1o9C/XtLZUY1OCvi07Y3FfJ6zbv2TqvvqdyV6Na25GblZClLRsb0zSjZ2HaUetXXvgbHKs7h/ylURvu4oEsHxf/uRqX+Vqp+NopQo6+ArLWEqKpCsiA0TkJRHZLiLFIrJERIaLSIvXR4pIq/vcRGSmiDTro7XL494LzGvtsxJJrAbOwlQ1pKGakbG6v0PknT3PfvVI4NBHTgfSCvdm5GYdafo0ozUSPumKNXv8VWCZqg5V1fOB3wBntvA+qQCqOqkN4cwEWtKf+SDWEsqkFcuBs7BQoMxNHOoRx5uinZaULcz0BRs+dTqWFtiAaeXGVMInXeBSwK+q88MHVLUIKA1/LyJDRGSliGywX5Ps41NF5H0ReRHYZB+ri3hvmYi8IiIeEXnBTvCIyDgRWS4i60VkqYgMFJFrgfHACyJSJCLHykd9CZfHXYXVzZCUYjlwFino21QZy/s7KaiBbm+WLTg7EPK3pNaBk+7MyM1KiF072qtkWAacCTS1PrwC+KaqNojIMOBvWAkSYCKQqao7TnLdRcAoYC+wGvi6iHyINSH8alWtFJEZwIOqepOI3AHcpaot2RzwUeAOWtZCTgj2wNnkI946Xlw+l/KDOwHhuil3ce6AUUfP27q3iCeX3kefHtauFWPOuYSrxt1AbX01C9/5HfXeOqZPuJHR51wCwIK3f8uMrDvp3d1a8Rvy72x3rdxIvlBD7yVlCxumD/5ZWYqkZjgdzynkZ+RmrXI6iPYuGZJuc6QBj4vIGCAIRO4iu+5LEm74vTIAESkChmBtvZMJvGs3fFOB8tYG5vK4690jXb8BnmvtPZwQOXD2yprHOX/wBG65YjaBoB9fwHvC+UMHZHLbVX9odGz9tkK+OvwKxg29lHlL7mH0OZewaecaBvcddjThqvoOow0JW8oxWuqDtQOW7nl257cG3bxfRBKxvkQ9MMvpIDqCZOhe2AKMa+Kc/wC+AEZjtXA7R7x3qupIkdkjiPWPkABbVHWM/bpAVa9oedjHuDzu50mgXYObEjlwVu87zPbyTVw8choAnVLTOK1L8yovpqZ0wh/wEgj6ERGCoSDvb1rM5aN/dPSckO+zYhr/frVbh/wHhhSWv1ipqm3b2z425mTkZpU2fZrRVsmQdAuBLiJya/iAiEwAIifS9wLKVTUE/IS2rd8vAfqJyMX2s9JEJPxZuhZo3h4sJ7rVvj6hHT9wduBQOelde7Fo2RxyX/kZLyyfi9d/4irnHV8U89DLtzJvyT2UV+0EYPx5l+Eu+5i8JfcwbdxPWbnlNSYO/yad046NyQV9WzrUKPl+b5lrdcU/tqnqiR8XnFNKEo89JJuET7r21tfXAN+0p4xtAWZj9cOGzQN+KiIfYHUttLr2p6r6gGuBh0XkE6AICM94eA6Y39yBtEguj3s3cE9r44qHkw2cBTVI6f7PyDr/u9xz7QK6dOrKu0UvNbpucN9hPHDd3/j1DxcyJfManlx6HwDduqRz21V/4D9/8ASD+w1j8+4PuOjcyby4/BGeemc2n+/bQihQnsh9nDGx58jWizYceHej3UhIBHdn5Ga163ohicSUdowju/RjITDV4VBO6mSlGg8dqWLuq3dw/3XWitBt5Z/ybtFLJ/TfRrrvhR9z9/efID2iQPff18zjwiFfp6KmjJCGGH/eZTyx5G7fz6cMbVXXQv66TyguryC9S2dmfetYyKs+28HqbbtIEcE1sD/TR7tOuNZTXsFrRcWEVPnqOYO5zGVV4nzhg43sq6nFNbA/0y60pg2/u+UzBvbuQeagAa0J85QuPH3KClfvr8VswUkz/TMjN+ubDsfQoSR8S7c9sUs/3gIk3EfqLyvV2PO0Mzg9vR9fVFvdfSV7NjKgd+MSCYeOVB3dEWFnhQdF6d6159H3K2rKqDl8gGFnjcYXaEDs/+q9B1v9EXv8ORncOrnx3pXbKvazZc8X5FyRxaxvTWHKiBN3cQ+FlFc3bOGWrInMunIKG3fvZV9NLXurDwGQc+Vkduyvot7n51B9A7urqmOScAE+Pbh88q66YieXCx/EmntuxFF7mb2QNFwe93b3SNe9wJ+cjiWsqRVnP/z6L3juvT8QDPnp23Mg10+9m5XFbwCQdf532Pj5ClYWv06qpJLWqQs3fuPeRjsivLHuGb4z0dr9YPx5l/Hk0vtYtnkxk4efuwdo1Uq0of36UHW48b9da7bt5lLXeXRKtbr0e3TtcsJ1u6uq6ZN+Gn3Srb3Kxpx9Flv2fkHmoDPxB4OEVAmElBQRlm7eyrcyh59wj2j6oPKNKd1Seyzv321wVGsTN1N2Rm7WHgee26GZ7gUHuEe6UoCVHOsrdow3rUfl6kkP+mO9AOJ4qqre6kf3g/Zr7T2qDh/h6ZUfHe1e+NM7Kxl11pl49lWSlprC9NEuzj6j8RTgT0rLKdlXyY8mXAjA+p1l7Kqq5vtjM3lt4xa2V1Yx9iuDGNa/D6u37Tp6XozpVYNuWduzc594/nl4ISM36/o4Ps+wme4FB7g87hBwEw4XxInXirOT0eC+rW1JuCcTDIWo9/n55TcmMf1CF39du4HmNCrCbfKrLxrFr67IYuqIc3l781auzBzOP4s/4y9rNvDB9t3RDPWEEN7e88yE+kBdSxbdtMVO4PY4Pcs4jkm6DnF53CVYK9UcUzT6l6tjVaqxKUHv5n3Rvmfv07qRmTEAEeHsPr1JQTjsbbwZRa9uXak+cmygvrq+gZ7dGpeV2LxnH4PP6IUvEGRfTR03TBrL+l178AVitymEEkp7s+xJlz/k3RKzh1j8wIyM3KykrgmSzEzSdZDL434ah/agisUeZy0R9G9v3gqLFhh11plsq9gPQGVtHYFQiO5dGk+OGHxGL/bXHeZA3RECwRBFu/cy6qxjtZOCoRArP9vJ1BFD8QeDhLumFSUYiu0Mr6D6uxeULhgYDAW2x/Axd2fkZq1rzonhOiUR388Ukcftr7NF5IaTXDOkpdX/ROR7ItLuVyWGmaTrvF8B78XzgbEu1dgU1YAXPdKmv2SL1m7ksffWUFl7mAfeeI8PP9/NxHMGU1V3hD++vZxFazfyLxNHIyLU1Dfw1Aorz6SmpHDN2EwWrljHH99ezujBAxnQ69h6l9XbdjF+SAadO6UysFcPVGHu0hUM6XM63TrHvuSvL1R/xpI9C08LaWhv02e32D8ycrMejcaNVHW+qv4lGvcCvgd0mKRrBtISgHuk6wxgHTA01s/ypfXYv2rSgz4n+nHDgr6tG/yHC8Y69fxk0Cut3+dXDrqxt4icEaVblgBfy8jNqm7uBSJSp6rpEd/PBMar6h0iMhuoU9W5x10zBHgLWIU1ULwHq3hUvb2q9N+wln1vw1o9OgYowCqBWgP8QFVj2dJ3nGnpJgC7BOR3ifEy4ZCkBD6ceG+ZkwkXIOjdnPDLoZ1W4688d9m+l8pVta7ps5tUDlzZkoRr62avviyyC0Ld38zrhgF5qjoKq4DUD+zji1V1gqqOBtzAzaq6BngdmGXXOmnXCRdM0k0YLo+7GPgxELOOQycHziKFAntaVIC+o6po2D1qbeXrJfbS9NaqBaZl5GbtasW19RGFn8YA9zXzuh12zWuwyrIOsb/OtOtebwKuwyqr2uGYpJtAXB53AXBvLO7t9MBZmIYO7wf/CKfjSBalhz3jiqoKP25lnQY/8P2M3KyiJs9sJREZHNEazrYPn6x6H1i1S+5Q1QuA3wMx240kkZmkm2BcHvdDWEXYo8bpgbNIQZ9nK8emxhrNsPXQx5O2Hvp4ZQsvU+CmjNysf8YipqMPUS2NaA3Pb+L0HkC5iKRhtXTD2lK9L+mYpJuYbgTejMaNfGk99sd6j7OWCPrcsZvs2o4VVRVOKT1csqwFl/w6IzdrUaziaaXfAh8C7wKeiOMvAbNEZKOIxHww2Wlm9kKCco90dQZexhpga5WQpARWT3pocyL044Y1HPyfvaBJt3VRovjGwOtX9O06qKnKZI9l5Gb9Mi4BGS1mWroJyuVxh+v6/qO190iUgbOwUKByh0m4bfNe+aJLav1Va09xyivAv8crHqPlTNJNYC6P2w/8CFjc0msTZeAsUtC3OaYFDDqIlLfLnh7XEDy84STv5QM/Nrv5JjaTdBOcnXhnYHU1NEt1r6HuRBk4ixT0bWvRbhvGyYUIdX6zdMFwf8hXHHH4aayE63cqLqN5TNJNAi6PO4A1hze/qXN9aT32bxhzZ69EGTgLUw0F0NoTt3EwWiWg/vQ3SxecGdTADuBR4FbTwk0OZiAtibhHulKBv2Al4BMk4sBZWNC/41N/3atxKU7bkXTv1Ps/s19YNMfpOIzmMy3dJOLyuIPADcDjJ3s/0QbOIgW9m6ucjqGdCQK3moSbfEzSTTIujzvo8rh/AdwGBMLHE3HgLFIosKuv0zG0I4eAq3PyC55yOhCj5UzSTVIuj3s+cAVQlagDZ2EaaqhBfaY/NzpKgIk5+QVRWTxjxJ9JuknM5XG/D0wsuvD28kQbOIsU9Je4gVSn42gHCrASbonTgRitZ5JuknN53NtDqV2+AyTaks+jgt7itlTJMqw6Cv8NfDcnv+CQ08EYbWNmL7QjedmFt2FNH+rc1Lnx1HDw0V0Q+orTcSSpSuDmnPyCN5wOxIgO09JtR26ff9kTwCVYVfkTQih4sMwk3FZ7C7jAJNz2xbR026G87MJuwGwgB4f7Uv31q1YGG9ZlORlDEmoAZuXkF5x0aqCR3EzSbcfysgvHYi0PdWzurrfm6bUaqrnYqecnoY3A9Tn5BcVNnmkkJdO90I7dPv+yDcAE4B6s1lNcqWpIQzUj4/3cJFWH9clkgkm47Ztp6XYQedmFw4CFQNwWUIT8pcW+upc7zNbabbAYuDMnv6DM6UCM2DNJtwPJyy4U4FZgDtAr1s/zHX5rWcjnnhrr5ySxncAdZqFDx2KSbgeUl13YH2sDzJ8Rw+llDdXzitCGhKwF4bAqIBd4LCe/IO7dPoazTNLtwPKyC4cA92NtEhjV/n1V32Fv9eNpJNicYYcdAf4MPJyTX1DjdDCGM0zSNcjLLswE7gN+QJSSb9Bb/JH/yNsTonGvdsCPNYvk/pz8gnKngzGcZZKucVRedqEL+A3wr7Rxfq+v9uXloUBpwlY9i5NDwJPAn80gmRFmkq5xgrzswqHALKxi6T1ac4+Gg3/eBsHzohpY8ijD6kZ40tRKMI5nkq7xpfKyC7tjbYx5E9by4mbRUO0+b83CATELLHGtxGrZ5ufkF5i9yoyTMknXaJa87MLhWMn3BmDgqc4N1H+4KtCwutlJOsntwdpC6dmc/ILPnA7GSHwm6RotkpddmApcBdwMfBtIO/4cb83zazR0YFK8Y4ujWmAJ8DzwTk5+QdDheIwk0qGTrogEgU1YiSOA9ZfoUVVt1a6qIlKnquknOf4cUKCqr7TwfsuAu1T149bEE2v2fN+rgWnA5UC6qqq3+tH9oP2cjS7q9gGvA/8ACnPyC7wOx2MkqU5OB+CwelUdAyAi/YEXsVZq/c7RqJLE7fMvq8BaWrwwL7uwM5CloQOTQb8H9AXE0QDbJgB8DBRi7djwQU5+QcdtoRhR09Fbuo1apiJyLvARVsLoAjwBjMf6C/grVX1fRGYC41X1DvuaAmCuqi4TkTpgAXApcBD4F1WtjGzpisg44E9AOrAfmKmqJ527abd0i4CJQE/gJlVdJyITsYqVdwPqgRtVtURERgHPYi1ISMGad7sX+D8gA2sa2AOqmt/2X71Te2TG9L5Yvw4XY/0aXoT1MyeqI8A6YDWwCliVk19Q52xIRnvU0Vu6jajq5yKSAvQHrrePXSAiI4F3RGR4E7foDmxQ1RwRuQ+rxXxH+E0RSQMeA662k/EM4EGsAaovvaeqThKRycAzQCbgASarakBELgf+gJVgs4E/q+oLItIZK8lOA/aq6rftGGJecwEgJ79gP/Cy/eKRGdNTgJFYCXg84ALOAwYT35q/CnwOfIrVtbTJ/npbTn5Bq7qVDKMlTNI9Ufgj8SVYCRJV9YjILqCppBsCwq3IRVjVoyKNwEqa74oIWMmmqRVKf7NjWCEiPUWkN9bc2edFZBhWEgkPZq0F/ktEMoDFqvqZiGwC5orIw1it7ZVNPC8m7IRWbL/+Ej7+yIzpnYEhWAl4KDAI6GO/+kb8Px3rz2sqJyZpL9bgVi1WicRaoBprvmzpca+ynPyC+hj8iIbRLCbpRrC7F4JABV/eHxmg8VLZU+3Ce3zfjQBbVLUlRb2Pv4cCDwDvq+o1IjIEWAagqi+KyIdYswqWisgtqlpod2lMAx4SkXdU9f4WPD+mcvILfMBW+9Vsdsu5E6BmTqyRTEzStYlIP2A+8LiqqoiswCoEU2h3K5wNlGD1rf7c7oYYhNXfGpYCXAu8hLWaa9VxjykB+onIxaq61u5uGK6qW04R2gzgfRG5BKhR1Rq7i2CP/f7MiJ/hXOBzVf1f++sLRcQDVKnqIrvPeSbtgN1yNrsMG0mnoyfdbiJSxLEpY3/FGuQCmAfMtz+eB7AGvLwishrYgdUXuBnYEHG/w8AoEVkP1GAlzKNU1Sci1wL/ayfOTlgDYqdKugdFZA32QJp9bA5W98KvsEbXw2YA14uIH2uK0/1YO0f8UURCWIVXbmveL41hGLHQoWcvGEZzRMznDvseVj/0Xao6/RTX9QZ+rKrzYhuhkUzMHmmG0bR6VR0T8drZzOt6Az+PYVxGEjJJNwGISJ6IFB33utHpuIzmEZHZInJXxPeb7QHOXGCo/fv5R/u9WSLykYh8KiK/dyZiw0kdvU83Iajq7U7HYJxSuO8fYIeqXtPM6+4BMiNWPV4BDMMafBXgdRGZrKoroh6xkbBM0jWMph1dLt5GV9ivjfb36VhJ2CTdDsQkXcNou+bO3RbgIVVdEPuQjERl+nQNo+12AmMBRGQscI59vJbGO28sBW4SkXT73EF2oSWjAzEtXcNou78DN9j9vh9hr65T1QMislpENgNvqeosEXEBa+1l4HVYNT4qHIrbcICZp2sYhhFHpnvBMAwjjkzSNQzDiCOTdA3DMOLIJF3DMIw4MknXMAwjjkzSNQzDiCOTdA3DMOLo/wGyZJuQwxkZ0AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot class distribution\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.set_title('Class Distribution', y=1.08)\n",
+    "ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',\n",
+    "       shadow=False, startangle=90)\n",
+    "ax.axis('equal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_samples = 2 * int(df['length'].sum() / 0.1) # Set number of samples to be 20% of the length in time of all data\n",
+    "prob_dist = class_dist / class_dist.sum() # Convert to range of [0,1]\n",
+    "choices = np.random.choice(class_dist.index, p=prob_dist) # Choose a class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = config(mode='conv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_data():\n",
+    "    # If there is data, load it in \n",
+    "    if os.path.isfile(config.p_path):\n",
+    "        print('Loading existing data for {} model'.format(config.mode))\n",
+    "        with open(config.p_path, 'rb') as handle:\n",
+    "            tmp = pickle.load(handle)\n",
+    "            return tmp\n",
+    "    else:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_rand_feat():\n",
+    "    \"\"\"\n",
+    "    This function creates a randomly generated and normalized 4-D feature vector\n",
+    "    \"\"\"\n",
+    "    tmp = check_data() # Load in old pickle data if it exists\n",
+    "    if tmp:\n",
+    "        print(config.mode)\n",
+    "        print(config.p_path)\n",
+    "        return tmp.data[0], tmp.data[1] # Returns our previously built randomized feature\n",
+    "    X = []\n",
+    "    y = []\n",
+    "    _min, _max = float('inf'), -float('inf')\n",
+    "    for _ in tqdm(range(n_samples)):\n",
+    "        rand_class = np.random.choice(class_dist.index, p=prob_dist) # Pick a random class based on our prob_dist probabilities\n",
+    "        file = np.random.choice(df[df.label==rand_class].index) # Choose a random file of class rand_class and get its index\n",
+    "        rate, wav = wavfile.read('data/clean/'+file)\n",
+    "        label = df.at[file,'label']\n",
+    "        rand_index = np.random.randint(0, wav.shape[0] - config.step) # Go to random time point in audio file\n",
+    "        sample = wav[rand_index:rand_index + config.step] # Grab a portion of our wav file at rand_index time point\n",
+    "        X_sample = mfcc(sample, rate,\n",
+    "                       numcep=config.nfeat, nfilt=config.nfilt, nfft = config.nfft)\n",
+    "        _min = min(np.amin(X_sample), _min) # Update min if our sample min is smaller\n",
+    "        _max = max(np.amax(X_sample), _max) # Update max if our sample max is larger\n",
+    "        X.append(X_sample) # Add our randomly selected sample to list X\n",
+    "        y.append(classes.index(label)) # Add the index corrosponding to our sample's label to list y\n",
+    "    config.min = _min # Save min\n",
+    "    config.max = _max # Save max\n",
+    "    X, y = np.array(X), np.array(y) \n",
+    "    X = (X - _min) / (_max - _min) # Normalize our input matrix X based on min and range, possible to normalize based on mean and standard deviation as well\n",
+    "    if config.mode == 'conv':\n",
+    "        X = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1)\n",
+    "    elif config.mode=='time':\n",
+    "        X = X.reshape(X.shape[0], X.shape[1], X.shape[2])\n",
+    "    y = to_categorical(y, num_classes = 10)\n",
+    "    config.data = (X, y) # Store the data\n",
+    "    \n",
+    "    with open(config.p_path, 'wb') as handle:\n",
+    "        pickle.dump(config, handle, protocol=2)\n",
+    "        \n",
+    "    return X, y\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_conv_model():\n",
+    "    model = Sequential()\n",
+    "    # Note that we only use a tenth of a second of our data, so we don't want to pool too much\n",
+    "    model.add(Conv2D(16, (3,3), activation='relu', strides=(1,1), # Strides only (1,1) due to small input space\n",
+    "                    padding='same', input_shape=input_shape))\n",
+    "    model.add(Conv2D(32, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(Conv2D(64, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(Conv2D(128, (3,3), activation='relu', strides=(1,1), \n",
+    "                    padding='same'))\n",
+    "    model.add(MaxPool2D((2,2), dim_ordering=\"th\"))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dense(64, activation='relu'))\n",
+    "    model.add(Dense(32, activation='relu'))\n",
+    "    model.add(Dense(10, activation='softmax')) # Final layer activate with softmax due to our categorical cross-entropy method\n",
+    "    model.summary()\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                 optimizer='adam',\n",
+    "                 metrics=['acc'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_recurrent_model():\n",
+    "    # Shape of data for RNN is (n,time,feat)\n",
+    "    model = Sequential()\n",
+    "    model.add(LSTM(128, return_sequences=True, input_shape=input_shape))\n",
+    "    model.add(LSTM(128, return_sequences=True))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(TimeDistributed(Dense(64, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(32, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(16, activation='relu')))\n",
+    "    model.add(TimeDistributed(Dense(8, activation='relu')))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "    model.summary()\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                 optimizer='adam',\n",
+    "                 metrics=['acc'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading existing data for conv model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D((2, 2), data_format=\"channels_first\")`\n",
+      "  if sys.path[0] == '':\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_1 (Conv2D)            (None, 9, 13, 16)         160       \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 9, 13, 32)         4640      \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 9, 13, 64)         18496     \n",
+      "_________________________________________________________________\n",
+      "conv2d_4 (Conv2D)            (None, 9, 13, 128)        73856     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 9, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 9, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 3456)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 128)               442496    \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 64)                8256      \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 32)                2080      \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 10)                330       \n",
+      "=================================================================\n",
+      "Total params: 550,314\n",
+      "Trainable params: 550,314\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Train on 23769 samples, validate on 2641 samples\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00001: val_acc improved from -inf to 0.75615, saving model to models\\conv.model\n",
+      "Epoch 2/10\n"
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+      "\n",
+      "Epoch 00002: val_acc improved from 0.75615 to 0.83643, saving model to models\\conv.model\n",
+      "Epoch 3/10\n"
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+      "\n",
+      "Epoch 00003: val_acc improved from 0.83643 to 0.89663, saving model to models\\conv.model\n",
+      "Epoch 4/10\n"
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+      "\n",
+      "Epoch 00004: val_acc improved from 0.89663 to 0.92389, saving model to models\\conv.model\n",
+      "Epoch 5/10\n"
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+      "\n",
+      "Epoch 00005: val_acc improved from 0.92389 to 0.92616, saving model to models\\conv.model\n",
+      "Epoch 6/10\n"
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+      "\n",
+      "Epoch 00006: val_acc improved from 0.92616 to 0.92881, saving model to models\\conv.model\n",
+      "Epoch 7/10\n"
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+      "\n",
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+      "Epoch 9/10\n"
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+      "\n",
+      "Epoch 00009: val_acc improved from 0.94320 to 0.95229, saving model to models\\conv.model\n",
+      "Epoch 10/10\n"
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00010: val_acc did not improve from 0.95229\n"
+     ]
+    }
+   ],
+   "source": [
+    "if config.mode == 'conv':\n",
+    "    X, y = build_rand_feat()\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2], 1) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_conv_model()\n",
+    "elif config.mode == 'time':\n",
+    "    X, y = build_rand_feat()\n",
+    "    \"\"\"\n",
+    "    TODO\n",
+    "    \n",
+    "    our rand_feat is saved as a pickle file and needs to be reshaped if\n",
+    "    it was originally made for conv model (similar if running conv and \n",
+    "    need to work with time)\n",
+    "    \"\"\"\n",
+    "    X = X.reshape(X.shape[0], X.shape[1], X.shape[2])\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2]) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_recurrent_model()\n",
+    "    \n",
+    "# Setup weights so that our gradient updates based on our class distribution \n",
+    "# (less bass drumbs => stronger gradient from these samples)\n",
+    "# This will give a little extra accuracy as well as reducing bias\n",
+    "class_weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)\n",
+    "\n",
+    "checkpoint = ModelCheckpoint(config.model_path, monitor='val_acc', verbose=1, mode='max',\n",
+    "                            save_best_only=True, save_weights_only=False, period=1)\n",
+    "\n",
+    "# Fit our model\n",
+    "model.fit(X, y, epochs=10, \n",
+    "         shuffle=True,\n",
+    "         class_weight=class_weight,\n",
+    "         validation_split=0.1,\n",
+    "         callbacks=[checkpoint])\n",
+    "\n",
+    "model.save(config.model_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config.mode = 'time'\n",
+    "config.model_path = os.path.join('models', config.mode + '.model')\n",
+    "config.p_path = os.path.join('pickles', config.mode + '.p')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████| 26410/26410 [02:01<00:00, 217.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(26410, 9, 13)\n",
+      "Model: \"sequential_7\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_11 (LSTM)               (None, 9, 128)            72704     \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 9, 128)            131584    \n",
+      "_________________________________________________________________\n",
+      "dropout_7 (Dropout)          (None, 9, 128)            0         \n",
+      "_________________________________________________________________\n",
+      "time_distributed_21 (TimeDis (None, 9, 64)             8256      \n",
+      "_________________________________________________________________\n",
+      "time_distributed_22 (TimeDis (None, 9, 32)             2080      \n",
+      "_________________________________________________________________\n",
+      "time_distributed_23 (TimeDis (None, 9, 16)             528       \n",
+      "_________________________________________________________________\n",
+      "time_distributed_24 (TimeDis (None, 9, 8)              136       \n",
+      "_________________________________________________________________\n",
+      "flatten_7 (Flatten)          (None, 72)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_34 (Dense)             (None, 10)                730       \n",
+      "=================================================================\n",
+      "Total params: 216,018\n",
+      "Trainable params: 216,018\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Train on 23769 samples, validate on 2641 samples\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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val_acc: 0.5112\n",
+      "\n",
+      "Epoch 00001: val_acc improved from -inf to 0.51117, saving model to models\\time.model\n",
+      "Epoch 2/10\n"
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+      "\n",
+      "Epoch 00002: val_acc improved from 0.51117 to 0.68648, saving model to models\\time.model\n",
+      "Epoch 3/10\n"
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val_acc: 0.7429\n",
+      "\n",
+      "Epoch 00003: val_acc improved from 0.68648 to 0.74290, saving model to models\\time.model\n",
+      "Epoch 4/10\n"
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+      "\n",
+      "Epoch 00004: val_acc did not improve from 0.74290\n",
+      "Epoch 5/10\n"
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+      "\n",
+      "Epoch 00005: val_acc improved from 0.74290 to 0.82431, saving model to models\\time.model\n",
+      "Epoch 6/10\n"
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val_acc: 0.8402\n",
+      "\n",
+      "Epoch 00006: val_acc improved from 0.82431 to 0.84021, saving model to models\\time.model\n",
+      "Epoch 7/10\n"
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+      "\n",
+      "Epoch 00007: val_acc did not improve from 0.84021\n",
+      "Epoch 8/10\n"
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+      "\n",
+      "Epoch 00008: val_acc did not improve from 0.84021\n",
+      "Epoch 9/10\n"
+     ]
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val_acc: 0.8633\n",
+      "\n",
+      "Epoch 00009: val_acc improved from 0.84021 to 0.86331, saving model to models\\time.model\n",
+      "Epoch 10/10\n"
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "\n",
+      "Epoch 00010: val_acc improved from 0.86331 to 0.87315, saving model to models\\time.model\n"
+     ]
+    }
+   ],
+   "source": [
+    "if config.mode == 'conv':\n",
+    "    X, y = build_rand_feat()\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2], 1) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_conv_model()\n",
+    "elif config.mode == 'time':\n",
+    "    X, y = build_rand_feat()\n",
+    "    print(X.shape)\n",
+    "    y_flat = np.argmax(y, axis=1) # Flatten out by grabbing collumn corrosponding to y label\n",
+    "    input_shape = (X.shape[1], X.shape[2]) # Grab input shape not including number of samples (since each input is one sample)\n",
+    "    model = get_recurrent_model()\n",
+    "    \n",
+    "# Setup weights so that our gradient updates based on our class distribution \n",
+    "# (less bass drumbs => stronger gradient from these samples)\n",
+    "# This will give a little extra accuracy as well as reducing bias\n",
+    "class_weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)\n",
+    "\n",
+    "checkpoint = ModelCheckpoint(config.model_path, monitor='val_acc', verbose=1, mode='max',\n",
+    "                            save_best_only=True, save_weights_only=False, period=1)\n",
+    "\n",
+    "# Fit our model\n",
+    "model.fit(X, y, epochs=10, \n",
+    "         shuffle=True,\n",
+    "         class_weight=class_weight,\n",
+    "         validation_split=0.1,\n",
+    "         callbacks=[checkpoint])\n",
+    "\n",
+    "model.save(config.model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Audi_Classification_ML/models/conv.model b/Audi_Classification_ML/models/conv.model
new file mode 100644
index 0000000..ce7d1c5
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diff --git a/Audi_Classification_ML/models/time.model b/Audi_Classification_ML/models/time.model
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index 0000000..9f7ff76
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diff --git a/Audi_Classification_ML/pickles/conv.p b/Audi_Classification_ML/pickles/conv.p
new file mode 100644
index 0000000..be3971a
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diff --git a/Audi_Classification_ML/pickles/time.p b/Audi_Classification_ML/pickles/time.p
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index 0000000..48f3da3
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diff --git a/Audi_Classification_ML/predict.ipynb b/Audi_Classification_ML/predict.ipynb
new file mode 100644
index 0000000..8de89ca
--- /dev/null
+++ b/Audi_Classification_ML/predict.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from tqdm import tqdm\n",
+    "from scipy.io import wavfile\n",
+    "from python_speech_features import mfcc\n",
+    "from keras.models import load_model\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_predictions(audio_dir):\n",
+    "    \"\"\"\n",
+    "    Function which takes an audio directory as input (which should contain a series of wav files), \n",
+    "    and returns true labels, predicted labels, and a dictionary of probabilites corrosponding to labels\n",
+    "    for each file.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    ----------\n",
+    "    audio_dir : location of folder containing wav files\n",
+    "    \n",
+    "    Returns:\n",
+    "    --------\n",
+    "    y_true : A list containing the true labels for each file\n",
+    "    \n",
+    "    y_pred : A list contianing predicted labels for each file\n",
+    "    \n",
+    "    fn_prob : A dict containing the probabilities  of each label for each file\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    y_true = [] # List containing actual labels\n",
+    "    y_pred = [] # List containing predicted labels\n",
+    "    fn_prob = {} # Dict containing probability of given label\n",
+    "    \n",
+    "    print('Extracting features from audio')\n",
+    "    for fn in tqdm(os.listdir(audio_dir)):\n",
+    "        rate, wav = wavfile.read(os.path.join(audio_dir, fn))\n",
+    "        label = fn2class[fn]\n",
+    "        c = classes.index(label) # Grab true label\n",
+    "        y_prob = []\n",
+    "        \n",
+    "        # Step through audio file in chunks\n",
+    "        for i in range(0, wav.shape[0]-config.step, config.step):\n",
+    "            sample = wav[i:i+config.step] # Take chunk of audio file\n",
+    "            x = mfcc(sample, rate, numcep=config.nfeat,\n",
+    "                     nfilt=config.nfilt, nfft = config.nfft) # Take mfcc of our sample\n",
+    "            x = (x - config.min) / (config.max - config.min) # Normalize based on max/min\n",
+    "            \n",
+    "            if config.mode == 'conv':\n",
+    "                x = x.reshape(1, x.shape[0], x.shape[1], 1)\n",
+    "            elif config.mode == 'time':\n",
+    "                x = np.expand_dims(x, axis=0)\n",
+    "            y_hat = model.predict(x)\n",
+    "            y_prob.append(y_hat)\n",
+    "            y_pred.append(np.argmax(y_hat))\n",
+    "            y_true.append(c)\n",
+    "            \n",
+    "        fn_prob[fn] = np.mean(y_prob, axis=0).flatten()\n",
+    "        \n",
+    "    return y_true, y_pred, fn_prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting features from audio\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [01:08<00:00,  4.36it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('data/instruments.csv')\n",
+    "classes = list(np.unique(df.label))\n",
+    "fn2class = dict(zip(df.fname, df.label))\n",
+    "p_path = os.path.join('pickles', 'conv.p')\n",
+    "\n",
+    "with open(p_path, 'rb') as handle:\n",
+    "    config = pickle.load(handle)\n",
+    "    \n",
+    "model = load_model(config.model_path)\n",
+    "\n",
+    "y_true, y_pred, fn_prob = build_predictions('data/clean')\n",
+    "\n",
+    "\n",
+    "acc_score = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
+    "\n",
+    "y_probs = []\n",
+    "# Iterate through each sample\n",
+    "for i, row in df.iterrows():\n",
+    "    y_prob = fn_prob[row.fname] # Grab probabilities for each class for the sample\n",
+    "    y_probs.append(y_prob)\n",
+    "    for c, p in zip(classes, y_prob):\n",
+    "        df.at[i, c] = p # add probabilities to dataframe\n",
+    "    \n",
+    "y_pred = [classes[np.argmax(y)] for y in y_probs]\n",
+    "df['y_pred'] = y_pred\n",
+    "\n",
+    "# Save as csv file which now contains all samples along with true label, predicted label, and probabilities for each class\n",
+    "df.to_csv('predictions.csv', index=False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Audi_Classification_ML/predictions.csv b/Audi_Classification_ML/predictions.csv
new file mode 100644
index 0000000..295b4a5
--- /dev/null
+++ b/Audi_Classification_ML/predictions.csv
@@ -0,0 +1,301 @@
+fname,label,Acoustic_guitar,Bass_drum,Cello,Clarinet,Double_bass,Flute,Hi-hat,Saxophone,Snare_drum,Violin_or_fiddle,y_pred
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