trainSirenDetector- 3Layer - 32-64-128.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Last updated: GHS\n",
    "# email: gabrielsarch@gmail.com\n",
    "#\n",
    "# Last edited: 4/25/2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file trains the Convolutional Neural Network\n",
    "## Uses siren, car, and background noises (split into 3-second chunks)\n",
    "\n",
    "# *******3-Layer architecture**********\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "RATE = 22050 #Hz - sample rate of training files\n",
    "\n",
    "# paths to numpy array files\n",
    "# These files contain numpy arrays in format (nSoundBytes x nSamples) \n",
    "# nSamples is usually = RATE*seconds\n",
    "# see generateTrainingData\n",
    "sirenFile = r'C:\\Users\\Gabe\\Documents\\SeniorDesign\\Data\\Senior Design data training\\sirenNoise.npy'\n",
    "backFile = r'C:\\Users\\Gabe\\Documents\\SeniorDesign\\Data\\Senior Design data training\\backNoise.npy'\n",
    "carFile = r'C:\\Users\\Gabe\\Documents\\SeniorDesign\\Data\\Senior Design data training\\carNoise.npy'\n",
    "\n",
    "# Where to save CNN Model WEIGHTS (not full architecture)\n",
    "saveModel = 'C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5'\n",
    "\n",
    "# Where to save model architecture + weights\n",
    "modelSave = 'C:/Users/Gabe/Documents/Driver-Alert/Models/siren_detector_V2_3Layer.h5'\n",
    "\n",
    "# Parameters for generateData\n",
    "num_samples = 999 # number of samples used for training from each array\n",
    "num_samp_noise = math.floor(num_samples/3)*4 # need more noise samples than siren samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shapes before:\n",
      "(1839, 66150)\n",
      "(6566, 66150)\n",
      "(613, 66150)\n",
      " \n",
      "Array shape after (valid, train):\n",
      "(184, 66150)\n",
      "(1655, 66150)\n",
      "(657, 66150)\n",
      "(5909, 66150)\n",
      "(61, 66150)\n",
      "(552, 66150)\n"
     ]
    }
   ],
   "source": [
    "# Load in numpy arrays containing data & splits data up into training and validation sets (use validation for ROC curves)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "sirenNoise = np.load(sirenFile)\n",
    "backNoise = np.load(backFile)\n",
    "carNoise = np.load(carFile)\n",
    "\n",
    "print('Array shapes before:')\n",
    "print(sirenNoise.shape)\n",
    "print(backNoise.shape)\n",
    "print(carNoise.shape)\n",
    "print(' ')\n",
    "\n",
    "percentHeldOut = 0.1\n",
    "\n",
    "perm = np.random.permutation(len(sirenNoise))[0:round(percentHeldOut*sirenNoise.shape[0])]\n",
    "sirenNoiseVal = sirenNoise[perm]\n",
    "sirenNoise = np.delete(sirenNoise, perm, axis=0)\n",
    "\n",
    "perm = np.random.permutation(len(backNoise))[0:round(percentHeldOut*backNoise.shape[0])]\n",
    "backNoiseVal = backNoise[perm]\n",
    "backNoise = np.delete(backNoise, perm, axis=0)\n",
    "\n",
    "perm = np.random.permutation(len(carNoise))[0:round(percentHeldOut*carNoise.shape[0])]\n",
    "carNoiseVal = carNoise[perm]\n",
    "carNoise = np.delete(carNoise, perm, axis=0)\n",
    "\n",
    "print('Array shape after (valid, train):')\n",
    "print(sirenNoiseVal.shape)\n",
    "print(sirenNoise.shape)\n",
    "print(backNoiseVal.shape)\n",
    "print(backNoise.shape)\n",
    "print(carNoiseVal.shape)\n",
    "print(carNoise.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = True\n",
    "isinstance(x,int) #or x==False\n",
    "if not isinstance(x, bool):\n",
    "    print('Hello')\n",
    "type(x)\n",
    "#raise TypeError('HI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "import pandas as pd\n",
    "import os\n",
    "import librosa\n",
    "\n",
    "sos = signal.butter(5, [50, 5000], 'bandpass', fs=RATE, output='sos')\n",
    "\n",
    "def get_mfccs(audio):\n",
    "   \n",
    "    try:\n",
    "        audio = 2*((audio-min(audio))/(max(audio)-min(audio)))-1\n",
    "\n",
    "        #Filter\n",
    "        audio = signal.sosfilt(sos, audio)\n",
    "        \n",
    "        mfccs = librosa.feature.mfcc(y=audio, sr=RATE, n_mfcc=40)\n",
    "\n",
    "    except Exception as e:\n",
    "            print(\"Error encountered for a file\")\n",
    "            return None\n",
    "\n",
    "    return mfccs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "## Fit #1\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint \n",
    "from datetime import datetime \n",
    "import random\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "\n",
    "\n",
    "def generateData(carNoise, backNoise, sirenNoise, uniformSNR=False):\n",
    "# This function \n",
    "# 1) combines siren and noise data to create 2 groups: siren present group, siren not present group\n",
    "# siren group: siren + car, siren + environment, siren + car + environmental (4 signal to noise ratios for each group)\n",
    "# no siren group: car, environment, car + environmental \n",
    "# Then\n",
    "# 2) Normalizes the audio and extracts\n",
    "\n",
    "# Inputs:\n",
    "# 1-3) car noises, background noises, siren noises (np array, nSoundBytes x nSamples)\n",
    "# 4) uniformSNR - if input = false (default) training data will consist of multiple SNRs (-5, 0, 5, 10 dB SNR)\n",
    "#               - if input = True will deafult to SNR of 0 dB\n",
    "#               - if input = Db (integer) will do SNR of that dB\n",
    "    Db5gain = 1.778279 # gain for 5dB increase\n",
    "\n",
    "    if isinstance(uniformSNR,int):\n",
    "       # if uniformSNR==True:\n",
    "        #    uniformSNR = 0.0001 # Set SNR to 0 if True\n",
    "        if not isinstance(uniformSNR, bool):\n",
    "            if uniformSNR > 0:\n",
    "                DbMult = uniformSNR/5 # Excludes if uniformSNR = False\n",
    "            elif uniformSNR < 0:\n",
    "                DbMult = 1/(-uniformSNR/5)\n",
    "                Db5gain = 1/Db5gain\n",
    "            else:\n",
    "                raise TypeError('Wrong uniformSNR input')\n",
    "    else:\n",
    "        raise TypeError('Wrong uniformSNR input')\n",
    "\n",
    "    start = datetime.now()\n",
    "\n",
    "    # get desired number of samples from each array\n",
    "    if len(carNoise) < num_samp_noise:\n",
    "        extra = num_samp_noise - len(carNoise)\n",
    "        perm = np.random.permutation(len(carNoise))\n",
    "        carX = np.concatenate((carNoise[perm], carNoise[perm[0:extra]]))\n",
    "        if len(carX) < num_samp_noise:\n",
    "            extra = num_samp_noise - len(carX)\n",
    "            perm = np.random.permutation(len(carX))\n",
    "            carX = np.concatenate((carX[perm], carX[perm[0:extra]]))\n",
    "    else:\n",
    "        perm = np.random.permutation(len(carNoise))[0:num_samp_noise]\n",
    "        carX = carNoise[perm]\n",
    "\n",
    "    if len(backNoise) < num_samp_noise:\n",
    "        extra = num_samp_noise - len(backNoise)\n",
    "        perm = np.random.permutation(len(backNoise))\n",
    "        envX = np.concatenate((backNoise[perm], backNoise[perm[0:extra]]))\n",
    "    else:\n",
    "        perm = np.random.permutation(len(backNoise))[0:num_samp_noise]\n",
    "        envX = backNoise[perm]\n",
    "\n",
    "    if len(sirenNoise) < num_samples:\n",
    "        extra = num_samples - len(sirenNoise)\n",
    "        perm = np.random.permutation(len(sirenNoise))\n",
    "        sirenX = np.concatenate((sirenNoise[perm], sirenNoise[perm[0:extra]]))\n",
    "    else:\n",
    "        perm = np.random.permutation(len(sirenNoise))[0:num_samples]\n",
    "        sirenX = sirenNoise[perm]\n",
    "\n",
    "\n",
    "    # Add gain to background noise\n",
    "    splitback1 = math.floor(num_samples/3)\n",
    "    \n",
    "    if uniformSNR == False:\n",
    "        \n",
    "        print(\"Mixing various SNRs\")\n",
    "\n",
    "        carX[0:splitback1] = carX[0:splitback1]*(1/(2*Db5gain))\n",
    "        carX[splitback1:splitback1*2] = carX[splitback1:splitback1*2]*(1/Db5gain)\n",
    "        carX[splitback1*2:splitback1*3] = carX[splitback1*2:splitback1*3]\n",
    "        carX[splitback1*3:] = carX[splitback1*3:]*Db5gain\n",
    "\n",
    "        envX[0:splitback1] = envX[0:splitback1]*(1/(2*Db5gain))\n",
    "        envX[splitback1:splitback1*2] = envX[splitback1:splitback1*2]*(1/Db5gain)\n",
    "        envX[splitback1*2:splitback1*3] = envX[splitback1*2:splitback1*3]\n",
    "        envX[splitback1*3:] = envX[splitback1*3:]*Db5gain\n",
    "\n",
    "        #sirenX[0:splitback1] = sirenX[0:splitback1]\n",
    "        #sirenX[splitback1:splitback1*2] = envX[splitback1:splitback1*2]*Db5gain\n",
    "        #sirenX[splitback1*2:splitback1*3] = envX[splitback1*2:splitback1*3]*Db5gain*2\n",
    "        #sirenX[splitback1*3:] = sirenX[splitback1*3:]*Db5gain*3\n",
    "        \n",
    "    elif uniformSNR == True: # SNR of 0 dB\n",
    "        print(\"SNR of 0 dB\")\n",
    "        sirenX = sirenX*1\n",
    "    \n",
    "    else:    \n",
    "        print(\"Uniform SNR with SNR = \", uniformSNR)\n",
    "        sirenX = sirenX*Db5gain*DbMult\n",
    "\n",
    "    sirencar = sirenX[0:splitback1]+carX[0:splitback1]\n",
    "    sirenenv = sirenX[splitback1:splitback1*2]+envX[0:splitback1]\n",
    "    sirencarenv = sirenX[splitback1*2:splitback1*3]+carX[splitback1:splitback1*2]*0.5+envX[splitback1:splitback1*2]*0.5\n",
    "\n",
    "    sirenSet = np.concatenate((sirencar, sirenenv, sirencarenv))\n",
    "    noiseSet = np.concatenate((carX[splitback1*2:splitback1*3], envX[splitback1*2:splitback1*3], carX[splitback1*3:splitback1*4]+envX[splitback1*3:splitback1*4]))            \n",
    "\n",
    "    print('Finished scrambling data set')\n",
    "    print('Extracting MFCCs...')\n",
    "\n",
    "    mfccSiren = []\n",
    "    for i in range(0,len(sirenSet)):\n",
    "        cur_audio = sirenSet[i]\n",
    "        #cur_audio = 2*((cur_audio-min(cur_audio))/(max(cur_audio)-min(cur_audio)))-1\n",
    "        mfcc = get_mfccs(cur_audio)\n",
    "        mfccSiren.append(mfcc)\n",
    "        sirenSet[i] = cur_audio\n",
    "\n",
    "    mfccNoise = []\n",
    "    for i in range(0,len(noiseSet)):\n",
    "        cur_audio = noiseSet[i]\n",
    "        #cur_audio = 2*((cur_audio-min(cur_audio))/(max(cur_audio)-min(cur_audio)))-1\n",
    "        mfcc = get_mfccs(cur_audio)\n",
    "        mfccNoise.append(mfcc)\n",
    "        noiseSet[i] = cur_audio\n",
    "    \n",
    "    # Create labels for the data\n",
    "    sirenLabels = ['siren']*len(mfccSiren) \n",
    "    noiseLabels = ['noise']*len(mfccNoise)\n",
    "\n",
    "    # Comibine MFCCs and labels \n",
    "    featureSiren = list(zip(mfccSiren, sirenLabels))\n",
    "    featureNoise = list(zip(mfccNoise, noiseLabels))\n",
    "    features = featureSiren + featureNoise\n",
    "    random.shuffle(features)\n",
    "    \n",
    "    # Convert to Panda dataframe\n",
    "    featuresdf = pd.DataFrame(features, columns=['feature','class_label'])\n",
    "\n",
    "    from sklearn.preprocessing import LabelEncoder\n",
    "    from keras.utils import to_categorical\n",
    "\n",
    "    # Convert features and corresponding classification labels into numpy arrays\n",
    "    X = np.array(featuresdf.feature.tolist())\n",
    "    y = np.array(featuresdf.class_label.tolist())\n",
    "\n",
    "    # Encode the classification labels\n",
    "    le = LabelEncoder()\n",
    "    yy = to_categorical(le.fit_transform(y)) \n",
    "\n",
    "    duration = datetime.now() - start\n",
    "    print(\"Generating training and test data took: \", duration)\n",
    "    \n",
    "    return X, yy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:02:12.825465\n",
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_17 (Conv2D)           (None, 39, 129, 32)       160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 19, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 19, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 18, 63, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 9, 31, 64)         0         \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 9, 31, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 8, 30, 128)        32896     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 4, 15, 128)        0         \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 4, 15, 128)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_2 ( (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 41,570\n",
      "Trainable params: 41,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "400/400 [==============================] - 1s 2ms/step\n",
      "Pre-training accuracy: 50.0000%\n"
     ]
    }
   ],
   "source": [
    "# Create model architecture\n",
    "\n",
    "# Format of input data\n",
    "num_rows = 40\n",
    "num_columns = 130 #x_train.shape[2]\n",
    "num_channels = 1\n",
    "\n",
    "# generate data for initial model evaluation\n",
    "X, yy = generateData(carNoise, backNoise, sirenNoise) \n",
    "\n",
    "# split the dataset \n",
    "x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2)#, random_state = 42)\n",
    "x_train = x_train.reshape(x_train.shape[0], num_rows, num_columns, num_channels)\n",
    "x_test = x_test.reshape(x_test.shape[0], num_rows, num_columns, num_channels)\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils\n",
    "from sklearn import metrics \n",
    "\n",
    "num_labels = 2 #yy.shape[1]\n",
    "filter_size = 2\n",
    "\n",
    "# Construct model \n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=filter_size, input_shape=(num_rows, num_columns, num_channels), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=filter_size, activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(filters=128, kernel_size=filter_size, activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(GlobalAveragePooling2D())\n",
    "\n",
    "model.add(Dense(num_labels, activation='softmax')) \n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer='adam') \n",
    "\n",
    "\n",
    "# Summary of model\n",
    "\n",
    "# Display model architecture summary \n",
    "model.summary()\n",
    "\n",
    "# Calculate pre-training accuracy \n",
    "score = model.evaluate(x_test, y_test, verbose=1)\n",
    "accuracy = 100*score[1]\n",
    "\n",
    "print(\"Pre-training accuracy: %.4f%%\" % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating data for fit # 1\n",
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:02:11.007959\n",
      "Train on 1598 samples, validate on 400 samples\n",
      "Epoch 1/25\n",
      "1598/1598 [==============================] - 13s 8ms/step - loss: 1.0317 - accuracy: 0.6940 - val_loss: 0.3005 - val_accuracy: 0.8825\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.30046, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 2/25\n",
      "1598/1598 [==============================] - 13s 8ms/step - loss: 0.2680 - accuracy: 0.8930 - val_loss: 0.2074 - val_accuracy: 0.9275\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.30046 to 0.20745, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 3/25\n",
      "1598/1598 [==============================] - 12s 7ms/step - loss: 0.3128 - accuracy: 0.8811 - val_loss: 0.2083 - val_accuracy: 0.9250\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.20745\n",
      "Epoch 4/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2391 - accuracy: 0.9018 - val_loss: 0.1955 - val_accuracy: 0.9150\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.20745 to 0.19549, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 5/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2243 - accuracy: 0.9061 - val_loss: 0.2277 - val_accuracy: 0.9050\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.19549\n",
      "Epoch 6/25\n",
      "1598/1598 [==============================] - 10s 6ms/step - loss: 0.2372 - accuracy: 0.8986 - val_loss: 0.2682 - val_accuracy: 0.8775\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.19549\n",
      "Epoch 7/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2017 - accuracy: 0.9199 - val_loss: 0.2155 - val_accuracy: 0.9175\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.19549\n",
      "Epoch 8/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2111 - accuracy: 0.9136 - val_loss: 0.2402 - val_accuracy: 0.8975\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.19549\n",
      "Epoch 9/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2117 - accuracy: 0.9149 - val_loss: 0.1871 - val_accuracy: 0.9250\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.19549 to 0.18707, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 10/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1897 - accuracy: 0.9218 - val_loss: 0.1900 - val_accuracy: 0.9100\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.18707\n",
      "Epoch 11/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.2153 - accuracy: 0.9111 - val_loss: 0.2520 - val_accuracy: 0.8975\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.18707\n",
      "Epoch 12/25\n",
      "1598/1598 [==============================] - 12s 8ms/step - loss: 0.2025 - accuracy: 0.9205 - val_loss: 0.2212 - val_accuracy: 0.9175\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.18707\n",
      "Epoch 13/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1896 - accuracy: 0.9230 - val_loss: 0.1940 - val_accuracy: 0.9225\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.18707\n",
      "Epoch 14/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1800 - accuracy: 0.9249 - val_loss: 0.1808 - val_accuracy: 0.9275\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.18707 to 0.18082, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 15/25\n",
      "1598/1598 [==============================] - 12s 7ms/step - loss: 0.1761 - accuracy: 0.9293 - val_loss: 0.1830 - val_accuracy: 0.9225\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.18082\n",
      "Epoch 16/25\n",
      "1598/1598 [==============================] - 13s 8ms/step - loss: 0.1962 - accuracy: 0.9186 - val_loss: 0.2107 - val_accuracy: 0.9350\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.18082\n",
      "Epoch 17/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1748 - accuracy: 0.9243 - val_loss: 0.1913 - val_accuracy: 0.9300\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.18082\n",
      "Epoch 18/25\n",
      "1598/1598 [==============================] - 14s 9ms/step - loss: 0.1657 - accuracy: 0.9318 - val_loss: 0.1834 - val_accuracy: 0.9300\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.18082\n",
      "Epoch 19/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1751 - accuracy: 0.9224 - val_loss: 0.1733 - val_accuracy: 0.9300\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.18082 to 0.17325, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 20/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1631 - accuracy: 0.9287 - val_loss: 0.1667 - val_accuracy: 0.9350\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.17325 to 0.16672, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 21/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1658 - accuracy: 0.9305 - val_loss: 0.1646 - val_accuracy: 0.9275\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.16672 to 0.16461, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 22/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1656 - accuracy: 0.9230 - val_loss: 0.2012 - val_accuracy: 0.9250\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.16461\n",
      "Epoch 23/25\n",
      "1598/1598 [==============================] - 11s 7ms/step - loss: 0.1538 - accuracy: 0.9293 - val_loss: 0.1637 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.16461 to 0.16370, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 24/25\n",
      "1598/1598 [==============================] - 10s 6ms/step - loss: 0.1455 - accuracy: 0.9374 - val_loss: 0.1579 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.16370 to 0.15785, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 25/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1543 - accuracy: 0.9305 - val_loss: 0.1810 - val_accuracy: 0.9250\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.15785\n",
      "Training completed in time:  0:04:45.941841\n",
      "Results from fit # 1\n",
      "Training Accuracy:  0.9430538415908813\n",
      "Testing Accuracy:  0.925000011920929\n",
      "Generating data for fit # 2\n",
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:02:08.342209\n",
      "Train on 1598 samples, validate on 400 samples\n",
      "Epoch 1/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1695 - accuracy: 0.9299 - val_loss: 0.1785 - val_accuracy: 0.9350\n",
      "\n",
      "Epoch 00001: val_loss did not improve from 0.15785\n",
      "Epoch 2/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1834 - accuracy: 0.9287 - val_loss: 0.1761 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.15785\n",
      "Epoch 3/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1401 - accuracy: 0.9456 - val_loss: 0.1441 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.15785 to 0.14408, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 4/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1406 - accuracy: 0.9437 - val_loss: 0.1279 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.14408 to 0.12791, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 5/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1488 - accuracy: 0.9406 - val_loss: 0.1650 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.12791\n",
      "Epoch 6/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1452 - accuracy: 0.9412 - val_loss: 0.1361 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.12791\n",
      "Epoch 7/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1355 - accuracy: 0.9449 - val_loss: 0.1418 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.12791\n",
      "Epoch 8/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1375 - accuracy: 0.9493 - val_loss: 0.1504 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.12791\n",
      "Epoch 9/25\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1320 - accuracy: 0.9437 - val_loss: 0.1380 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.12791\n",
      "Epoch 10/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1371 - accuracy: 0.9474 - val_loss: 0.1279 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.12791 to 0.12788, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 11/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1349 - accuracy: 0.9468 - val_loss: 0.1220 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.12788 to 0.12195, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 12/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1196 - accuracy: 0.9549 - val_loss: 0.1322 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.12195\n",
      "Epoch 13/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1231 - accuracy: 0.9518 - val_loss: 0.1369 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.12195\n",
      "Epoch 14/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1206 - accuracy: 0.9531 - val_loss: 0.1279 - val_accuracy: 0.9575\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.12195\n",
      "Epoch 15/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1121 - accuracy: 0.9581 - val_loss: 0.1510 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.12195\n",
      "Epoch 16/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1066 - accuracy: 0.9581 - val_loss: 0.1558 - val_accuracy: 0.9350\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.12195\n",
      "Epoch 17/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1061 - accuracy: 0.9599 - val_loss: 0.1658 - val_accuracy: 0.9275\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.12195\n",
      "Epoch 18/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1037 - accuracy: 0.9606 - val_loss: 0.1255 - val_accuracy: 0.9575\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.12195\n",
      "Epoch 19/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1219 - accuracy: 0.9549 - val_loss: 0.1369 - val_accuracy: 0.9575\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.12195\n",
      "Epoch 20/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1017 - accuracy: 0.9637 - val_loss: 0.1250 - val_accuracy: 0.9625\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.12195\n",
      "Epoch 21/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1104 - accuracy: 0.9606 - val_loss: 0.1515 - val_accuracy: 0.9325\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.12195\n",
      "Epoch 22/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1020 - accuracy: 0.9618 - val_loss: 0.1391 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.12195\n",
      "Epoch 23/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1108 - accuracy: 0.9587 - val_loss: 0.1363 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.12195\n",
      "Epoch 24/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1375 - accuracy: 0.9512 - val_loss: 0.1408 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.12195\n",
      "Epoch 25/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0903 - accuracy: 0.9668 - val_loss: 0.1346 - val_accuracy: 0.9600\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.12195\n",
      "Training completed in time:  0:03:44.138608\n",
      "Results from fit # 2\n",
      "Training Accuracy:  0.9730913639068604\n",
      "Testing Accuracy:  0.9599999785423279\n",
      "Generating data for fit # 3\n",
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:01:55.223875\n",
      "Train on 1598 samples, validate on 400 samples\n",
      "Epoch 1/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1617 - accuracy: 0.9456 - val_loss: 0.1982 - val_accuracy: 0.9325\n",
      "\n",
      "Epoch 00001: val_loss did not improve from 0.12195\n",
      "Epoch 2/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1359 - accuracy: 0.9481 - val_loss: 0.2268 - val_accuracy: 0.9250\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.12195\n",
      "Epoch 3/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1279 - accuracy: 0.9537 - val_loss: 0.1743 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.12195\n",
      "Epoch 4/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1317 - accuracy: 0.9531 - val_loss: 0.2646 - val_accuracy: 0.9075\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.12195\n",
      "Epoch 5/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1296 - accuracy: 0.9456 - val_loss: 0.2034 - val_accuracy: 0.9350\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.12195\n",
      "Epoch 6/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1100 - accuracy: 0.9549 - val_loss: 0.1784 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.12195\n",
      "Epoch 7/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1018 - accuracy: 0.9625 - val_loss: 0.1500 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.12195\n",
      "Epoch 8/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1181 - accuracy: 0.9543 - val_loss: 0.1554 - val_accuracy: 0.9600\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.12195\n",
      "Epoch 9/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1002 - accuracy: 0.9606 - val_loss: 0.1432 - val_accuracy: 0.9600\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.12195\n",
      "Epoch 10/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0901 - accuracy: 0.9637 - val_loss: 0.1291 - val_accuracy: 0.9675\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.12195\n",
      "Epoch 11/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0854 - accuracy: 0.9681 - val_loss: 0.1715 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.12195\n",
      "Epoch 12/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0876 - accuracy: 0.9631 - val_loss: 0.1627 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.12195\n",
      "Epoch 13/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0869 - accuracy: 0.9643 - val_loss: 0.2150 - val_accuracy: 0.9375\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.12195\n",
      "Epoch 14/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0983 - accuracy: 0.9662 - val_loss: 0.1790 - val_accuracy: 0.9375\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.12195\n",
      "Epoch 15/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0960 - accuracy: 0.9556 - val_loss: 0.1555 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.12195\n",
      "Epoch 16/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0803 - accuracy: 0.9662 - val_loss: 0.1841 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.12195\n",
      "Epoch 17/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0832 - accuracy: 0.9681 - val_loss: 0.1813 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.12195\n",
      "Epoch 18/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0784 - accuracy: 0.9681 - val_loss: 0.1974 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.12195\n",
      "Epoch 19/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0870 - accuracy: 0.9712 - val_loss: 0.1590 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.12195\n",
      "Epoch 20/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0680 - accuracy: 0.9712 - val_loss: 0.1738 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.12195\n",
      "Epoch 21/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0684 - accuracy: 0.9762 - val_loss: 0.1788 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.12195\n",
      "Epoch 22/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0653 - accuracy: 0.9737 - val_loss: 0.1576 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.12195\n",
      "Epoch 23/25\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0689 - accuracy: 0.9718 - val_loss: 0.1743 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.12195\n",
      "Epoch 24/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0516 - accuracy: 0.9800 - val_loss: 0.1554 - val_accuracy: 0.9575\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.12195\n",
      "Epoch 25/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0658 - accuracy: 0.9725 - val_loss: 0.1615 - val_accuracy: 0.9575\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.12195\n",
      "Training completed in time:  0:03:43.013615\n",
      "Results from fit # 3\n",
      "Training Accuracy:  0.9893617033958435\n",
      "Testing Accuracy:  0.9574999809265137\n",
      "Generating data for fit # 4\n",
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:01:50.379629\n",
      "Train on 1598 samples, validate on 400 samples\n",
      "Epoch 1/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1576 - accuracy: 0.9449 - val_loss: 0.1143 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00001: val_loss improved from 0.12195 to 0.11425, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 2/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1455 - accuracy: 0.9493 - val_loss: 0.1545 - val_accuracy: 0.9375\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.11425\n",
      "Epoch 3/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1204 - accuracy: 0.9524 - val_loss: 0.1065 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.11425 to 0.10648, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 4/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1197 - accuracy: 0.9543 - val_loss: 0.1319 - val_accuracy: 0.9300\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.10648\n",
      "Epoch 5/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1051 - accuracy: 0.9625 - val_loss: 0.1293 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.10648\n",
      "Epoch 6/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1000 - accuracy: 0.9618 - val_loss: 0.1223 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.10648\n",
      "Epoch 7/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.1119 - accuracy: 0.9518 - val_loss: 0.1107 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.10648\n",
      "Epoch 8/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.1008 - accuracy: 0.9650 - val_loss: 0.1194 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.10648\n",
      "Epoch 9/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0854 - accuracy: 0.9681 - val_loss: 0.1218 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.10648\n",
      "Epoch 10/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0791 - accuracy: 0.9700 - val_loss: 0.1001 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.10648 to 0.10008, saving model to C:/Users/Gabe/Documents/SeniorDesign/CNNModels/weights.best.basic_cnn.hdf5\n",
      "Epoch 11/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0861 - accuracy: 0.9693 - val_loss: 0.1507 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.10008\n",
      "Epoch 12/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0936 - accuracy: 0.9618 - val_loss: 0.1222 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.10008\n",
      "Epoch 13/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0746 - accuracy: 0.9718 - val_loss: 0.1120 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.10008\n",
      "Epoch 14/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0818 - accuracy: 0.9668 - val_loss: 0.1040 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.10008\n",
      "Epoch 15/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0787 - accuracy: 0.9725 - val_loss: 0.1204 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.10008\n",
      "Epoch 16/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0820 - accuracy: 0.9675 - val_loss: 0.1414 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.10008\n",
      "Epoch 17/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0787 - accuracy: 0.9725 - val_loss: 0.1447 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.10008\n",
      "Epoch 18/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0868 - accuracy: 0.9625 - val_loss: 0.1714 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.10008\n",
      "Epoch 19/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0671 - accuracy: 0.9743 - val_loss: 0.1457 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.10008\n",
      "Epoch 20/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0610 - accuracy: 0.9793 - val_loss: 0.1539 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.10008\n",
      "Epoch 21/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0591 - accuracy: 0.9737 - val_loss: 0.1517 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.10008\n",
      "Epoch 22/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0629 - accuracy: 0.9775 - val_loss: 0.1388 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.10008\n",
      "Epoch 23/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0587 - accuracy: 0.9812 - val_loss: 0.1293 - val_accuracy: 0.9500\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.10008\n",
      "Epoch 24/25\n",
      "1598/1598 [==============================] - 9s 6ms/step - loss: 0.0592 - accuracy: 0.9737 - val_loss: 0.1965 - val_accuracy: 0.9425\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.10008\n",
      "Epoch 25/25\n",
      "1598/1598 [==============================] - 9s 5ms/step - loss: 0.0535 - accuracy: 0.9819 - val_loss: 0.1371 - val_accuracy: 0.9375\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.10008\n",
      "Training completed in time:  0:03:43.362650\n",
      "Results from fit # 4\n",
      "Training Accuracy:  0.9893617033958435\n",
      "Testing Accuracy:  0.9375\n",
      "Finished model fitting\n"
     ]
    }
   ],
   "source": [
    "# Fit model by regenerating training data based on num_fits\n",
    "# Each iteration of num_fits, a new combination of siren and noise is generated from the array\n",
    "# adn the model weights are fit with epochs = num_epochs and batch size = num_batch_size\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint \n",
    "from datetime import datetime \n",
    "\n",
    "#num_epochs = 12\n",
    "#num_batch_size = 128\n",
    "\n",
    "num_fits = 4 # number of times new training data is generated and model trained\n",
    "\n",
    "# For each fit (i.e. for iterations = num_fits):\n",
    "num_epochs = 25\n",
    "num_batch_size = 32\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=saveModel, monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "for i in range(0, num_fits):\n",
    "    \n",
    "    print('Generating data for fit #', i+1)\n",
    "\n",
    "    X, yy = generateData(carNoise, backNoise, sirenNoise)\n",
    "    \n",
    "    # split the dataset \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2)#, random_state = 42)\n",
    "    x_train = x_train.reshape(x_train.shape[0], num_rows, num_columns, num_channels)\n",
    "    x_test = x_test.reshape(x_test.shape[0], num_rows, num_columns, num_channels)\n",
    "    \n",
    "    start = datetime.now()\n",
    "\n",
    "    model.fit(x_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(x_test, y_test), callbacks=[checkpointer], verbose=1)\n",
    "\n",
    "    duration = datetime.now() - start\n",
    "    print(\"Training completed in time: \", duration)\n",
    "\n",
    "    # Evaluating the model on the training and testing set\n",
    "    \n",
    "    print('Results from fit #', i+1)\n",
    "    score = model.evaluate(x_train, y_train, verbose=0)\n",
    "    print(\"Training Accuracy: \", score[1])\n",
    "\n",
    "    score = model.evaluate(x_test, y_test, verbose=0)\n",
    "    print(\"Testing Accuracy: \", score[1])\n",
    "    \n",
    "print('Finished model fitting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model\n",
    "\n",
    "model.save(modelSave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loads model\n",
    "# Do not have to run if already trained\n",
    "from keras.models import load_model\n",
    "\n",
    "modelSave = 'C:/Users/Gabe/Documents/Driver-Alert/Models/siren_detector_V2_3Layer.h5' # location of the trained model\n",
    "model = load_model(modelSave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve for validation data\n",
    "\n",
    "num_samples = sirenNoiseVal.shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC for various SNRs\n",
    "\n",
    "num_samples = sirenNoiseVal.shape[0]\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from numpy import ravel\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_rows = 40\n",
    "num_columns = 130 #x_train.shape[2]\n",
    "num_channels = 1\n",
    "\n",
    "def plotROC(sirenSNR, Title):\n",
    "\n",
    "    num_samples = sirenNoiseVal.shape[0]\n",
    "    X, yy = generateData(carNoiseVal, backNoiseVal, sirenNoiseVal, sirenSNR)\n",
    "\n",
    "    X = X.reshape(X.shape[0], num_rows, num_columns, num_channels)\n",
    "\n",
    "    y_pred_keras = model.predict_proba(X)\n",
    "    predicted_proba = y_pred_keras[:,1]\n",
    "    desc_score_indices = np.argsort(predicted_proba, kind=\"mergesort\")[::-1]\n",
    "    y_pred = predicted_proba\n",
    "\n",
    "    y_test2 = yy[:,1]\n",
    "\n",
    "    y_pred_keras = model.predict_proba(X)\n",
    "\n",
    "    fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test2, y_pred)\n",
    "\n",
    "    auc_keras = auc(fpr_keras, tpr_keras)\n",
    "\n",
    "    # plot\n",
    "    plt.figure(1)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.plot(fpr_keras, tpr_keras, label='ROC (area = {:.3f})'.format(auc_keras))\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.title(Title)\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mixing various SNRs\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:24.459748\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mixed SNRs: (-5, 0, 5, 10 dB SNR)\n",
    "\n",
    "plotROC(False, 'ROC curve: Mixed SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  -10\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:21.629604\n"
     ]
    },
    {
     "data": {
      "image/png": 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07NmTqKgoT2OygnjZrHDhwowdO5aRI0eSmJhIhw4d+N///pc6FHLq1CmefPJJnnvuOQCeffZZhg0bxs8//wzAuXPneO211y543ZiYGOLj41NvHzlyhJIlnU9OM2bMSDeeli1bMnHiRBITEwFnbuLEiRMcOXKEa665hjx58vDNN9+wY4f/Uutz5sxh9erVF3ylTQjg9FBmzpyJqrJ06VIKFy6c4dDRgw+e35ksU6YMX331FQCbNm3i9OnTFCtWjDJlyvD111+jqpw4cYKlS5emzsEcPHiQYsWKWY2jIJi9bCftJ33PgHnrUidjMxPOn+iD4aqrrqJu3bqsX7+e3r17e54QAOeTZCh91a5dW9PauHHjBffltCuuuOK8261bt9aZM2eqquratWu1cePGWrlyZa1YsaIOHjxYz507l9r2o48+0lq1ammVKlU0JiZG+/bte8HrnzhxQmNjY1Of9+GHH2r58uW1YcOG2rdvX23cuLGqqg4aNEhfffXV1OclJydr//79tVq1alq1alVt0qSJHj58WPfv368333yz1q5dW+Pi4rRKlSq6bdu2S/oenDt3Tnv06KEVKlTQatWq6fLly1Mfq1Gjxnlty5cvr5s2bTrvvg0bNmj9+vX1hhtu0Bo1auiiRYtUVfXYsWN67733amxsrMbExOiIESNSn/PBBx9onz59/MaTG34vQtn9b36n1QZ9pve/+Z2+s3SH1+GEhcTERB0xYoQOHTo09T7fvwXBBKzQAP7GivoZB87N6tSpo2l3Xtu0aRMxMTEeRZRzevfuzV133UXz5s29DiXXuOeee/jHP/7B9ddff8FjkfJ7kR38DRHZUFD2WrNmDXFxcaxcuZL777+f9957L0fLsYjISlXNdCmTDR+FkAEDBnDy5Emvw8g1zp49y9133+03IZiLkzJB7MuGgrLHmTNnePHFF6lTpw67du3igw8+yPGEcDHCZqJZM1jlEy6uvfZa2rRp43UYuUbevHn9zm2A/5VQkSjQSWLrFQTPli1beOWVV3jooYd47bXXKFKkiNchZSgskkL+/Pk5ePCglc82wB/7KUTyiqSUZBDo1brWK8hex48fZ/78+XTo0IFq1arx008/pa6Wy+3CIimUKlWKhIQE9u/f73UoJpdI2XktEs1etpMB89YBuetq3UjxxRdf0LVrV3bs2EGtWrWIiYkJmYQAYZIU8uTJYztsGeNKGS4a1q66JYMcdOjQIfr27cu0adOoXLky//nPf0JyoUNYJAVjzPnqlr/aEkIOSk5OpkGDBvz888/079+fgQMHhuzwpSUFY4zJogMHDqQWsBs2bBhlypS5oFx8qLGkYEwIymhVUbCqiZo/qCqzZs3iqaeeYvjw4XTt2pW7777b67CyhV2nYEwI8nddQQpbSRRcO3bsoFWrVnTu3JmYmBhuueUWr0PKVtZTMCYXyuz6AruuwBv//Oc/6d69e2qhyR49enDZZeH12dqSgjHZJDv3Esjs+gLrDXijWLFiNGjQgEmTJlG2bFmvwwmKsKh9ZExu0H7S99k6nm/XF3gvMTGRUaNGkZiYyIsvvgiEbvWEQGsfWU/BmGxkQzrh48cffyQuLo4ff/yRBx54IDUZhGJCuBiWFIzJgoyqiprQdvr0aYYMGcKIESMoWrQo//rXv7jnnnu8DivHhNcMiTE5IKWMRNqNZ2ycPzzEx8czcuRIOnXqxKZNmyIqIYD1FIy5aFZGIvwcP36cefPm0bFjR6pVq8bmzZsjtnSOJQVj/Mjs4jArIxE+Fi1aRNeuXdm1axd16tQhJiYmYhMC2PCRMX7ZxWHh7+DBg3Tu3Jnbb7+dAgUK8N///jckC9hlN+spGOMjpYdgF4eFt5QCdvHx8Tz//PO88MILIVvALrtZUjDGh29CsN5A+Nm/fz9FihQhKiqKV155hbJly1KzZk2vw8pVbPjImDRSegg2ZxA+VJW3336bypUr89ZbbwHQtm1bSwh+WFIwxoS17du307JlSx599FGqV69O06ZNvQ4pV7PhIxOx7AK08Ddr1iy6d++OiDBhwgQef/zxsCtgl93su2Milr8VRjaXEF6uvfZabrnlFjZs2ED37t0tIQTAegombFn56ciTmJjIiBEjSE5OZuDAgbRo0YIWLVp4HVZIsaRgwoK/BGDlpyPLqlWrePTRR1mzZg0PPfRQyFYz9ZolBRMWfJeSpqhb/morPx0BTp06xUsvvcTIkSMpVqwY8+bNC5utMb0Q1KQgIrcDrwNRwBRVHZ7m8TLADOBKt00/VV0YzJhMaLKhIJOerVu38tprr/HII4/w6quvctVVV3kdUkgL2qyLiEQB44FWQCzwoIjEpmn2AvC+qt4IPABMCFY8JrRlVHYCbCgo0hw9epTp06cDULVqVbZs2cKUKVMsIWSDYPYUbgLiVXUrgIi8B7QFNvq0USClv18Y2BPEeEyIs56AAVi4cCHdunVj9+7d1K1bl5iYmLDdGtMLwVyfVRLY5XM7wb3P12DgYRFJABYCvfy9kIh0FZEVIrJi//79wYjVGJPLHThwgI4dO3LnnXdSsGBBlixZYgXsgiCYPQV/0/5pN4R+EJiuqqNEpB4wS0Sqqeq5856kOhmYDM4ezUGJ1uQadlGZSSulgN3WrVsZOHAgAwYMIF++fF6HFZaCmRQSgNI+t0tx4fBQHHA7gKp+LyL5gaLAviDGZXI5fyuJbM4gMv32228UK1aMqKgoRo4cSdmyZbnhhhu8DiusBTMpLAcqiUh5YDfORPJDadrsBJoB00UkBsgP2PiQsfmDCKeqTJs2jWeeeYbhw4fTrVs37rrrLq/DighBm1NQ1SSgJ7AI2ISzymiDiAwRkTZus2eAx0RkDfAu8Iiq2vBQhJq9bCftJ32f4SojE/62bt1K8+bN6dKlCzVr1qR58+ZehxRRgnqdgnvNwcI09w30Od4INAhmDCZ3850/8L0C2YaKItOMGTPo0aMHUVFRvPnmmzz22GNWryiH2RXNxhMpycA3EdgVyKZEiRLceuutTJw4kVKlSnkdTkSypGA8kTKZbIkgsp09e5bhw4dz7tw5Bg8ezG233cZtt93mdVgRzZKCyTG+Q0VWlsIsX76cRx99lPXr19OxY0crYJdL2GCdyTG+pSpsiWnkOnnyJH379uXmm2/m0KFDLFiwgJkzZ1pCyCWsp2BylPUOzLZt2xg3bhyPPfYYr7zyCoULF/Y6JOPDkoIxJuiOHDnCv//9b/76179StWpV4uPjKV26dOZPNDnOho+MMUH1ySefULVqVbp06cJPP/0EYAkhF7OkYIwJiv3799OhQwdat27NVVddxffff0+VKlW8DstkwoaPTNClrDqyonaRIzk5mYYNG7Jt2zZeeukl+vXrR968eb0OywTAkoIJGn8XqNmKo/D266+/cs011xAVFcWoUaMoV64c1apV8zoscxFs+MgEje8FasPaVWfO4/XsIrUwde7cOSZNmkTlypWZNGkSAK1bt7aEEIIy7SmIyOXAU0BZVe0mItcBlVT106BHZ0KeLUENf/Hx8Tz22GN8++233HrrrbRs2dLrkMwlCKSnMA1nw5yG7u09wLCgRWTCwuxlO1OHjUz4evvtt6levTqrVq3irbfe4ssvv6RChQpeh2UuQSBzCpVU9UERuQ9AVU+KXXoYsfztiuZPSkKwOYTwVqZMGVq2bMn48eMpWdJ+1uEgkKRw1t0RTQHcTXPOBjUqk2sFuorICt2FpzNnzvCPf/yDc+fOMWTIEJo1a0azZs28Dstko0CSwt+Bz4BSIjIDaAx0CWpUJtdJu6zU5gkiz7Jly4iLi2PDhg107tzZCtiFqUyTgqp+KiIrgPo4cwvPqqrtoRwhbFmpOXHiBC+++CJjxoyhZMmSfPzxx9x5551eh2WCJJDVR5+ragtgvp/7TIjLbI4gbTKw4aDIs2PHDiZMmEC3bt0YPnw4hQrZBYjhLN2kICJ5gfzAtSJSEKeXAFAIsL8MIc5fD8AfSwaR6fDhw8ydO5cuXboQGxtLfHy87YQWITLqKTwB9AGuATbwR1I4CrwZ5LhMkNnOZyY98+fPp3v37uzbt4+GDRtSpUoVSwgRJN2koKqjgdEi8pSqjsnBmEwOsQlj42vfvn08+eSTzJkzhxtuuIEFCxZYAbsIFMhE8xgRqQLE4gwnpdw/O5iBGWNyTnJyMg0aNGDnzp0MHTqU5557jjx58ngdlvFAIBPNLwAtgCrAIqAl8D/AkoIxIW7Pnj38+c9/Jioqitdff51y5coRGxvrdVjGQ4GUuWgPNAX2qmpHoAZWXdWYkHbu3DkmTpxIlSpVePNNZ4rwjjvusIRgAkoKp1Q1GUhyVyH9ClhxE2NC1M8//0zTpk3p0aMHdevWpVWrVl6HZHKRQJLCjyJyJU5hvBXAD8CqoEZljAmKqVOnUqNGDdauXcu0adP4/PPPKV++vNdhmVwkw2Egt/DdYFU9DIwXkUVAIVW1pGBMCCpXrhytWrVi/PjxFC9e3OtwTC6UYVJQVRWRj4Ha7u34HInKGJMtzpw5w9///ncAhg4dagXsTKYCGT76QURqBT0Sk2Nsr4PI8N1331GzZk1efvll9u7di6p6HZIJAYEkhYY4iWGziKwSkR9FxIaPQlhKrSMrbBeejh8/Tu/evWnYsCEnT57ks88+Y+rUqVbR1AQkkKWld2f1xUXkduB1IAqYoqrD/bS5HxiMs1/DGlV9KKvvZwJXt/zVVtoiTO3cuZNJkybxxBNPMGzYMAoWLOh1SCaEBHJF8y9ZeWERiQLGA7cBCcByEVmgqht92lQC+gMNVPWQiFyTlfcyGUtbCTWQTXJMaDl06BAffPABXbt2JTY2lq1bt1KiRAmvwzIhKJDho6y6CYhX1a2qehZ4D2ibps1jwHhVPQRg+zQER0rxuxSxxQvZ0FEYmTdvHrGxsfTo0YPNmzcDWEIwWRbMK5NLArt8bicAddO0qQwgIktwhpgGq+pnaV9IRLoCXcHZE9ZcPCt+F35+/fVXevXqxdy5c6lZsyaffPIJ119/vddhmRAXUFIQkVJAJVX9RkTyAdGqeiKzp/m5L+3yh2igEtAEKAX8V0SquddF/PEk1cnAZIA6derYEgoT8ZKTk2nUqBG7du1i2LBh9O3b1wrYmWwRSEG8R4GeQGGgIlAWmAA0z+SpCUBpn9ulgD1+2ixV1URgm4hsxkkSywOK3mQqZflpepvomNCSkJBAiRIliIqKYuzYsZQvX97KW5tsFcicwpPAzTib66CqP+NsvJOZ5UAlESnv7uL2ALAgTZsPcYrtISJFcYaTtgYWusnI7GU7aT/pewbMWwfY8tNQd+7cOcaNG0eVKlWYOHEiAK1atbKEYLJdIMNHp1X1bMoaZ3dVUaYLnlU1SUR64pTbjgKmqeoGERkCrFDVBe5jLURkI5AMPKuqB7N4LsaH7awWPn766Se6dOnCkiVLaNmyJa1bt/Y6JBPGAkkKS0TkOSC/iDTF2abz40BeXFUXAgvT3DfQ51hxtvzsE3DEJmA2uRz6pkyZQs+ePSlQoAAzZsygY8eOdhGaCapAksJzOCt/fgJ643y6nxTMoMzFSXsdAti1COGiYsWK3HXXXbzxxhtce+21XodjIkAgSeEOnKuRJwY7GJM1KUNFvknArkUITadPn2bIkCEADBs2jKZNm9K0aVOPozKRJJCkcD/whoh8jXMB2pfupjsmF/BdXWRDRaFtyZIlxMXFsXnzZrp06YKq2lCRyXGBlLno6F6bcCfwKDBZRD5V1W5Bj85cIO1QUUq1U+sVhK5jx44xYMAAxo8fT9myZVm0aBEtWrTwOiwToQIqc6GqZ4D5wHScpab3BzEmk4G0JSvqlr+aYe2q2+qiEJaQkMCUKVPo1asX69ats4RgPBXIxWvNca4xaA4sAWYCVsnUAzZUFD4OHjzI+++/T/fu3YmJiWHr1q22E5rJFQLpKXQDPgNiVLWDqi5wC9yZHGb7IIQ+VWXu3LnExsby5JNPphaws4RgcotA5hTuzYlATPpS5hFSLkazoaLQtHfvXp544gnmzZtH7dq1+fzzz62Ancl10k0KIvIfVW0sIoc4v5Cd4Fx3ZsV0cojvklPrJYSmlAJ2u3fvZsSIETz99NNERwfmf2XvAAAaLUlEQVSzSLExWZPRb2XK4uiiORGIyZhdnRyadu3aRcmSJYmKimL8+PGUL1+eypUrex2WMelKd05BVc+5h1NVNdn3C5iaM+FFtpSidr6rjUxoSE5OZuzYsecVsGvZsqUlBJPrBdJ/vcH3hlsQ7/+CE45JMXvZztQKpylF7Uxo2LRpE3FxcXz//fe0atWKu+66y+uQjAlYRnMKfwP6AQVF5PeUu3HmF6ynEGQpK43sGoTQMnnyZHr16kXBggWZNWsWHTp0sKuSTUjJaEnqCKAYMNr9txhQVFWvVtVncyK4SGcrjUJPpUqVaNeuHRs3buThhx+2hGBCTkbDR9ep6hYRmQVUTbkz5ZdcVdcGOTZjcr1Tp04xePBgRIThw4dbATsT8jJKCv2AOGC8n8cUuCUoEUU432sSrPR17rZ48WK6dOnCli1b6NatmxWwM2Eh3aSgqnHuv41yLhxj1yTkfkePHqVfv35MnDiRChUq8NVXX3Hrrbd6HZYx2SLTMhcico+IFHSP+4nI+yJSI/ihRa6UaxJsPiF32rNnD9OnT6dPnz6sXbvWEoIJK4HUPhqsqsdEpD5wFzAH23nNRJgDBw4wYcIEAKpUqcK2bdsYNWoUV1xxhceRGZO9AkkKKRvqtAYmqOq/gHzBC8mY3ENVmTNnDrGxsTz11FP8/PPPALY1pglbgSSFvSIyHqd89kIRyRvg84wJaXv27OHuu+/mgQceoGzZsqxcudKuSDZhL9DtOO8AxqnqIREpgbMyyVyitLuoAbbqKJdITk7mlltuYffu3YwcOZLevXtbATsTEQIpnX1cRDYCTUSkCfBfVf006JFFAH9LT23Vkbd27NhBqVKliIqKYsKECVSoUIHrrrvO67CMyTGB7LzWE+gBfOje9b6IjFfVCUGNLEJY9dPcITk5mddff50XXniBESNG0LNnT9sW00SkQPrDXYGbVPU4gIgMA74DLClkkV2glrusX7+euLg4fvjhB1q3bs3dd9/tdUjGeCaQCWMBEn1uJ7r3mSyyC9RyjzfffJNatWqxdetWZs+ezYIFCyhVqpTXYRnjmUB6CrOApSLyL5xkcDcwI6hRRQAbNvJWSkmKmJgY7rvvPsaMGUOxYsW8DssYzwUy0TxCRL4BUspddFPV5cENK/z4rjSyYSPvnDx5koEDBxIVFcUrr7xC48aNady4sddhGZNrBLrG7oz7dc791wQoJRks2+ZsSVG3/NU2bOSRb7/9li5duvDLL7/Qo0cPK2BnjB+BrD56HngImIczfDRbRN5R1X8EO7hQ52/3NKtnlPOOHDnCc889x+TJk6lYsSJff/21lbc2Jh2B9BQeBmqr6kkAEXkZWAlYUsiE7Z6WO+zdu5d//vOf9O3bl5deeokCBQp4HZIxuVYgq492cH7yiAa2BvLiInK7iGwWkXgRSfcqaBG5V0RUROoE8rqhxHZP88b+/fsZN24c4BSw2759O6+++qolBGMyEUhSOAlsEJEpIvIWsA44LCKvichr6T1JRKJwNuhpBcQCD4pIrJ92BYEngWVZOYHcaPaynbSf9D0b9x71OpSIo6rMnj2bmJgYnnnmmdQCdrayyJjABDJ89In7lWJpgK99ExCvqlsBROQ9oC2wMU27v+PsB903wNfN9ew6BG/s2rWL7t2788knn1C3bl2mTp1qBeyMuUiBLEmdmsXXLgns8rmdANT1bSAiNwKlVfVjEUk3KYhIV5wrqylTJjSGYuw6hJyVlJREkyZN+PXXXxk9ejS9evUiKirK67CMCTnBLPvob62fpj4ochkwGngksxdS1cnAZIA6depoJs09Y+Urct727dspXbo00dHRTJo0iQoVKlChQgWvwzImZAVzX4QEoLTP7VLAHp/bBYFqwLcish24GVgQypPNNmyUc5KSkhg5ciQxMTGpO6I1b97cEoIxlyjgnoKI5FPVi7lwbTlQSUTKA7txNul5KOVBVT0CFPV5/W+Bvqq64iLeI9exYaPgW7t2LXFxcaxYsYK2bdvyl7/8xeuQjAkbmfYUROQmEVkHbHFv1xCRcZk9T1WTgJ7AImAT8L6qbhCRISLS5hLjNhFqwoQJ1K5dmx07djBnzhzmzZtHiRIlvA7LmLARSE9hLM7+zB8CqOoaEQnoclBVXQgsTHPfwHTaNgnkNU1kSilJUa1aNR544AFGjx5N0aJFM3+iMeaiBJIULlPVHWlqxCQHKZ6QNXvZTpZt+5265a/2OpSwcuLECV544QWio6N59dVXueWWW7jlllu8DsuYsBXIRPMuEbkJUBGJEpGngJ+DHFfISSlpYRPM2eerr76ievXqjBkzhjNnzqCaaxeeGRM2AkkK3YE+QBngN5xVQt2DGVSospIW2ePw4cN06dKF5s2bEx0dzeLFixk7dqxVNDUmBwRy8do+nJVDxg+7NiH7/fbbb7z33nv87W9/Y9CgQVx++eVeh2RMxAikdPZb+Fx0lkJVuwYlohBj1yZkj5RE0Lt3b66//nq2b99uE8nGeCCQieYvfY7zA+04v3xFxLNrE7JOVXnnnXfo3bs3x48f54477qBSpUqWEIzxSCDDR3N8b4vILOCLoEVkIsbOnTvp1q0bn376KfXq1WPq1KlUqlTJ67CMiWhZqX1UHiib3YGYyJJSwG7fvn2MHTuWHj16WAE7Y3KBQOYUDvHHnMJlwO9AuhvmRAqbYM6arVu3UrZsWaKjo3nrrbeoWLEi5cqV8zosY4wrw6QgzhrAGji1iwDOaQQvFk9JBADLtv0O/LH3sslYUlISo0aNYtCgQYwYMYInn3ySZs2aeR2WMSaNDJOCqqqIzFPV2jkVUG7m2zNISQZ2XULmVq9eTVxcHKtWraJdu3bcd999XodkjElHIHMKP4hILVVdFfRoQoCtNLo4b7zxBk8//TRFihRh7ty5VtHUmFwu3aQgItFupdOGwGMi8gtwAmfzHFXVWjkUowlBKQXsbrjhBjp06MBrr73G1VdbXShjcruMego/ALWAu3MollzLJpUDd/z4cZ5//nny5MnDyJEjrYCdMSEmo9pHAqCqv/j7yqH4cgW7ajkwn3/+OdWqVWPcuHEkJiZaATtjQlBGPYViItInvQdV9bUgxJNr2VxC+g4dOkSfPn2YPn06119/PYsXL6Zhw4Zeh2WMyYKMkkIU8CfcHkOk8V1+asNGGdu3bx9z586lf//+DBw4kPz583sdkjEmizJKCntVdUiORZLL+A4Z2bDRhX799Vfeffddnn766dQCdkWKFPE6LGPMJcooKURkD8GXDRldSFWZOXMmTz/9NCdPnqR169ZUqlTJEoIxYSKjiWa73NScZ/v27dx+++088sgjxMbGsnr1aitgZ0yYSbenoKq/52QgJndLSkqiadOmHDhwgPHjx9OtWzcuuyyQjfuMMaEkK1VSw5pdk3C++Ph4ypcvT3R0NNOmTaNChQqULWtFco0JV/ZRz8fsZTsZMG8dy7b9HvGTy4mJiQwbNoyqVasyfvx4AJo2bWoJwZgwZz0F/ugdpFQ+HdauekQXulu1ahVxcXGsXr2a++67j/bt23sdkjEmh1hS4I/lp1b5FMaOHUufPn0oVqwY//73v2nXrp3XIRljcpAlBVekLz9NKWB344030qlTJ0aNGsVVV13ldVjGmBxmSSHCHTt2jP79+5MvXz5GjRpFo0aNaNSokddhGWM8YhPNEeyzzz6jWrVqTJgwAVW1AnbGGEsKkejgwYN07tyZVq1accUVV7BkyRJee+01nN1XjTGRzJJCBDp48CDz5s3jxRdf5Mcff6RevcidSzHGnC+oSUFEbheRzSISLyL9/DzeR0Q2ishaEflKRGwRfJDs3buXkSNHoqpUrlyZHTt2MGTIEPLly+d1aMaYXCRoSUFEooDxQCsgFnhQRGLTNPsRqKOqNwBzgRHBiidSqSrTpk0jJiaGF198kfj4eABbWWSM8SuYPYWbgHhV3aqqZ4H3gLa+DVT1G1U96d5cCpQKYjwRZ9u2bbRo0YK4uDhq1KjBmjVrrICdMSZDwVySWhLY5XM7AaibQfs44FN/D4hIV6ArQJkykXth2cVISkri1ltv5eDBg0ycOJGuXbtaATtjTKaCmRT8LWXxu+ZRRB4G6gCN/T2uqpOByQB16tSxdZMZ2LJlCxUqVCA6Opq3336bihUrUrp0aa/DMsaEiGB+dEwAfP8alQL2pG0kIs2B54E2qnomiPGEtcTERIYOHUq1atV44403AGjSpIklBGPMRQlmT2E5UElEygO7gQeAh3wbiMiNwCTgdlXdF8RY/AqXMtkrVqwgLi6OtWvX8sADD/Dggw96HZIxJkQFraegqklAT2ARsAl4X1U3iMgQEWnjNnsV+BPwgYisFpEFwYrHH9+EEKplsl9//XXq1q3LgQMHmD9/Pu+++y7XXHON12EZY0JUUGsfqepCYGGa+wb6HDcP5vsHIlQL4aUUsKtTpw5xcXGMGDGCK6+80uuwjDEhzgrihZijR4/yt7/9jfz58zN69GgaNGhAgwYNvA7LGBMmbI1iCFm4cCFVq1Zl8uTJREdHWwE7Y0y2s6QQAg4cOMDDDz/MnXfeSeHChfnuu+949dVXrYCdMSbbRWxSmL1sZ+r2m7ndoUOH+Oijjxg0aBCrVq2ibt2MrgE0xpisi9g5hfmrdwPk2lVHu3fv5p133uHZZ5+lUqVK7NixwyaSjTFBF3E9hdnLdtJ+0vepezLntv2YVZW33nqL2NhYBg8ezC+//AJgCcEYkyMiLink5msTfvnlF5o1a0bXrl2pVasWa9eu5brrrvM6LGNMBInI4aPceG1CUlISzZo14/fff2fSpEl06dLFCtgZY3JcRCaF3GTz5s1UrFiR6OhoZsyYQcWKFSlVyiqIG2O8YR9FPXL27Fleeuklqlevzvjx4wFo3LixJQRjjKesp+CBH374gbi4ONavX89DDz1Ehw4dvA7JGGMA6ynkuDFjxlCvXr3Uaw/eeecdihYt6nVYxhgDWFLIMSklKW666SYee+wxNmzYQOvWrT2OyhhjzmfDR0F25MgRnnvuOS6//HLGjBlD/fr1qV+/vtdhGWOMX9ZTCKKPPvqI2NhYpkyZQr58+ayAnTEm17OkEAT79+/noYceok2bNhQpUoSlS5fyyiuvWAE7Y0yuZ0khCI4cOcLChQt56aWXWLFiBf/3f//ndUjGGBMQm1PIJrt27eKf//wn/fr147rrrmPHjh0ULlzY67CMMeaiWE/hEp07d44333yTqlWrMnTo0NQCdpYQjDGhyJLCJdiyZQu33nor3bt356abbmLdunVWwM4YE9Js+CiLkpKSuO222zh8+DBTp07lr3/9q00kG2NCniWFi7Rp0yYqVapEdHQ0s2bNomLFipQoUcLrsIwxJlvY8FGAzpw5w6BBg7jhhht44403AGjUqJElBGNMWLGeQgCWLl1KXFwcGzdupGPHjnTs2NHrkIwxJiisp5CJUaNGUb9+fY4dO8bChQuZOXMmRYoU8TosY4wJCksK6Th37hwA9erVo1u3bqxfv55WrVp5HJUxxgSXDR+lcfjwYZ555hkKFCjAuHHjrICdMSaiWE/Bx4cffkhsbCwzZsygYMGCVsDOGBNxIqanMHvZTuav3s3GvUeJLV7ovMf27dtHz549+eCDD6hZsyYff/wxtWrV8ihSY4zxTsT0FHwTQtuaJc977OjRo3zxxRe8/PLL/PDDD5YQjDERK2J6CgCxxQsx5/F6AOzcuZNZs2YxYMAArrvuOnbu3EnBggU9jtAYY7wV1J6CiNwuIptFJF5E+vl5PJ+IzHEfXyYi5YIZDziriiZMmEDVqlUZNmxYagE7SwjGGBPEpCAiUcB4oBUQCzwoIrFpmsUBh1T1OmA08Eqw4gE4deokTZo04YknnqBevXps2LDBCtgZY4yPYPYUbgLiVXWrqp4F3gPapmnTFpjhHs8FmkmQqsqpKmvXrmXdunW8/fbbLFq0iHLlygXjrYwxJmQFc06hJLDL53YCUDe9NqqaJCJHgCLAAd9GItIV6ApQpkyZLAVTtWRhrqpbjcEvb6R48eJZeg1jjAl3wUwK/j7xp134H0gbVHUyMBmgTp06Wbp4YNBdVYGqWXmqMcZEjGAOHyUApX1ulwL2pNdGRKKBwsDvQYzJGGNMBoKZFJYDlUSkvIjkBR4AFqRpswDo7B7fC3ytdhmxMcZ4JmjDR+4cQU9gERAFTFPVDSIyBFihqguAqcAsEYnH6SE8EKx4jDHGZC6oF6+p6kJgYZr7BvocnwbuC2YMxhhjAhcxZS6MMcZkzpKCMcaYVJYUjDHGpLKkYIwxJpWE2gpQEdkP7Mji04uS5mrpCGDnHBnsnCPDpZxzWVUtllmjkEsKl0JEVqhqHa/jyEl2zpHBzjky5MQ52/CRMcaYVJYUjDHGpIq0pDDZ6wA8YOccGeycI0PQzzmi5hSMMcZkLNJ6CsYYYzJgScEYY0yqsEwKInK7iGwWkXgR6efn8XwiMsd9fJmIlMv5KLNXAOfcR0Q2ishaEflKRMp6EWd2yuycfdrdKyIqIiG/fDGQcxaR+92f9QYRmZ3TMWa3AH63y4jINyLyo/v7fYcXcWYXEZkmIvtEZH06j4uIjHW/H2tFpFa2BqCqYfWFU6b7F6ACkBdYA8SmadMDeNM9fgCY43XcOXDOTYEC7nH3SDhnt11BYDGwFKjjddw58HOuBPwIXOXevsbruHPgnCcD3d3jWGC713Ff4jnfAtQC1qfz+B3Apzg7V94MLMvO9w/HnsJNQLyqblXVs8B7QNs0bdoCM9zjuUAzEfG3NWioyPScVfUbVT3p3lyKsxNeKAvk5wzwd2AEcDongwuSQM75MWC8qh4CUNV9ORxjdgvknBUo5B4X5sIdHkOKqi4m4x0o2wIz1bEUuFJEsm3j+XBMCiWBXT63E9z7/LZR1STgCFAkR6ILjkDO2VcczieNUJbpOYvIjUBpVf04JwMLokB+zpWByiKyRESWisjtORZdcARyzoOBh0UkAWf/ll45E5pnLvb/+0UJ6iY7HvH3iT/tuttA2oSSgM9HRB4G6gCNgxpR8GV4ziJyGTAaeCSnAsoBgfyco3GGkJrg9Ab/KyLVVPVwkGMLlkDO+UFguqqOEpF6OLs5VlPVc8EPzxNB/fsVjj2FBKC0z+1SXNidTG0jItE4Xc6Mumu5XSDnjIg0B54H2qjqmRyKLVgyO+eCQDXgWxHZjjP2uiDEJ5sD/d2er6qJqroN2IyTJEJVIOccB7wPoKrfA/lxCseFq4D+v2dVOCaF5UAlESkvInlxJpIXpGmzAOjsHt8LfK3uDE6IyvSc3aGUSTgJIdTHmSGTc1bVI6paVFXLqWo5nHmUNqq6wptws0Ugv9sf4iwqQESK4gwnbc3RKLNXIOe8E2gGICIxOElhf45GmbMWAJ3cVUg3A0dUdW92vXjYDR+papKI9AQW4axcmKaqG0RkCLBCVRcAU3G6mPE4PYQHvIv40gV4zq8CfwI+cOfUd6pqG8+CvkQBnnNYCfCcFwEtRGQjkAw8q6oHvYv60gR4zs8Ab4nI0zjDKI+E8oc8EXkXZ/ivqDtPMgjIA6Cqb+LMm9wBxAMngb9m6/uH8PfOGGNMNgvH4SNjjDFZZEnBGGNMKksKxhhjUllSMMYYk8qSgjHGmFSWFEyuJSLJIrLa56tcBm3LpVdVMqeJSB0RGeseNxGR+j6PdRORTjkYS81QrxpqclbYXadgwsopVa3pdRAXy71ALuUiuSbAceA797E3s/v9RCTareHlT02csiYLs/t9TXiynoIJKW6P4L8issr9qu+nTVUR+cHtXawVkUru/Q/73D9JRKL8PHe7iLzitvtBRK5z7y8rzj4UKftRlHHvv09E1ovIGhFZ7N7XREQ+dns23YCn3fdsJCKDRaSviMSIyA9pzmute1xbRP4jIitFZJG/CpgiMl1EXhORb4BXROQmEflOnD0FvhOR690rgIcA7d33by8iV4hTr3+529ZfZVkTybyuHW5f9pXeF84Vuavdr3nufQWA/O5xJZyrWgHK4dafB8YBHdzjvMDlQAzwEZDHvX8C0MnPe24HnnePOwEfu8cfAZ3d40eBD93jdUBJ9/hK998mPs8bDPT1ef3U2+55VXCP/wa8gHPl6ndAMff+9jhX8aaNczrwMRDl3i4ERLvHzYF/ucePAG/4PG8Y8HBKvMDPwBVe/6ztK/d82fCRyc38DR/lAd4QkZo4SaOyn+d9DzwvIqWAf6vqFhFpBtQGlrtlPi4H0qsB9a7Pv6Pd43rAPe7xLJw9GgCWANNF5H3g3xdzcjhF3O4HhuP88W8PXI9TyO8LN84oIL26Nh+oarJ7XBiY4faKFLcsgh8tgDYi0te9nR8oA2y6yNhNmLKkYELN08BvQA2c4c8LNs9R1dkisgy4E1gkIl1wyg3PUNX+AbyHpnN8QRtV7SYidd33Wu0mq0DNwalF9W/npXSLiFQHNqhqvQCef8Ln+O/AN6razh22+jad5wjwF1XdfBFxmghicwom1BQG9qpTK78jzifp84hIBWCrqo7FqSh5A/AVcK+IXOO2uVrS36e6vc+/37vH3/FH4cQOwP/c16moqstUdSBwgPNLGgMcwynjfQFV/QWnt/MiToIAp9R1MXH2BUBE8ohI1XTi9FUY2O0eP5LB+y8CeonbDRGneq4xqSwpmFAzAegsIktxho5O+GnTHlgvIquBKjhbF27EGbP/3J3Q/QJIbwvDfG5PozdOzwTgSeCv7nM7uo8BvCoi69zlsItx9hD29RHQLmWi2c97zQEe5o/9AM7ilHN/RUTW4Mw7XDCZ7scI4B8isoTzE+U3QGzKRDNOjyIPsNaN+e8BvLaJIFYl1Rgf4mzIU0dVD3gdizFesJ6CMcaYVNZTMMYYk8p6CsYYY1JZUjDGGJPKkoIxxphUlhSMMcaksqRgjDEm1f8DKG7x2Is5eOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of -10 dB\n",
    "\n",
    "plotROC(-10, 'ROC curve: -10 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  -5\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:19.886817\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of -5 dB\n",
    "\n",
    "plotROC(-5, 'ROC curve: -5 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR of 0 dB\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:22.216620\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of 0 dB\n",
    "\n",
    "plotROC(True, 'ROC curve: 0 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  5\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:20.735734\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of 5 dB\n",
    "\n",
    "plotROC(5, 'ROC curve: 5 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  10\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:20.991411\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of 10 dB\n",
    "\n",
    "plotROC(10, 'ROC curve: 10 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  15\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:21.127090\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of 15 dB\n",
    "\n",
    "plotROC(15, 'ROC curve: 15 dB SNR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform SNR with SNR =  20\n",
      "Finished scrambling data set\n",
      "Extracting MFCCs...\n",
      "Generating training and test data took:  0:00:22.773780\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR of 20 dB\n",
    "\n",
    "plotROC(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotROC(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_keras, tpr_keras, label='ROC (area = {:.3f})'.format(auc_keras))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test various sounds\n",
    "\n",
    "import sounddevice as sd\n",
    "import time\n",
    "startPlay = 0 # Where to start index\n",
    "amountPlay = 5 # seconds\n",
    "for i in list(range(startPlay, amountPlay+startPlay)):\n",
    "    sd.play(sirenSet[i,:], RATE)\n",
    "    time.sleep(3) "
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}