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voice_activity_detection.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import tree\n",
"import random\n",
"import copy\n",
"import os\n",
"import graphviz\n",
"import scipy.io.wavfile as wav\n",
"from src.voice_activity_detection.extract_features import extract_features"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1356 entries, 0 to 1355\n",
"Data columns (total 9 columns):\n",
"RMS 1356 non-null int64\n",
"ZCR 1356 non-null float64\n",
"audio 1356 non-null object\n",
"bandwidth 1356 non-null float64\n",
"nwpd 1356 non-null float64\n",
"rse 1353 non-null float64\n",
"spectral_centroid 1356 non-null float64\n",
"spectral_flux 1356 non-null float64\n",
"spectral_rolloff 1356 non-null float64\n",
"dtypes: float64(7), int64(1), object(1)\n",
"memory usage: 95.4+ KB\n"
]
}
],
"source": [
"voice_noise_data = np.load(\"numpy_files/musan_mean_speech_noise.npy\").item()\n",
"voice_noise_df = pd.DataFrame.from_dict(voice_noise_data)\n",
"voice_noise_df.replace([np.inf,-np.inf,np.nan],0)\n",
"voice_noise_df.dropna()\n",
"voice_noise_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RMS</th>\n",
" <th>ZCR</th>\n",
" <th>audio</th>\n",
" <th>bandwidth</th>\n",
" <th>nwpd</th>\n",
" <th>rse</th>\n",
" <th>spectral_centroid</th>\n",
" <th>spectral_flux</th>\n",
" <th>spectral_rolloff</th>\n",
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" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>120</td>\n",
" <td>0.317919</td>\n",
" <td>1</td>\n",
" <td>2.852637e+05</td>\n",
" <td>-1.086645</td>\n",
" <td>-0.190269</td>\n",
" <td>535.891491</td>\n",
" <td>0.039433</td>\n",
" <td>4020.624371</td>\n",
" </tr>\n",
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" <td>0.442350</td>\n",
" <td>1</td>\n",
" <td>6.743143e+05</td>\n",
" <td>10.745700</td>\n",
" <td>-0.875232</td>\n",
" <td>840.023296</td>\n",
" <td>0.061607</td>\n",
" <td>3833.170732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13</td>\n",
" <td>0.464951</td>\n",
" <td>1</td>\n",
" <td>8.572244e+05</td>\n",
" <td>1.387262</td>\n",
" <td>-0.322468</td>\n",
" <td>572.973659</td>\n",
" <td>0.018836</td>\n",
" <td>4938.532110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>130</td>\n",
" <td>0.150850</td>\n",
" <td>1</td>\n",
" <td>3.938587e+06</td>\n",
" <td>0.498670</td>\n",
" <td>-0.290313</td>\n",
" <td>3448.585720</td>\n",
" <td>0.010311</td>\n",
" <td>6988.571429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>42</td>\n",
" <td>0.250210</td>\n",
" <td>1</td>\n",
" <td>6.657314e+05</td>\n",
" <td>0.925133</td>\n",
" <td>-0.263716</td>\n",
" <td>745.102831</td>\n",
" <td>0.014008</td>\n",
" <td>4590.849315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>93</td>\n",
" <td>0.481068</td>\n",
" <td>1</td>\n",
" <td>7.152688e+05</td>\n",
" <td>-0.090272</td>\n",
" <td>-0.184233</td>\n",
" <td>738.235253</td>\n",
" <td>0.022868</td>\n",
" <td>4529.035025</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>12</td>\n",
" <td>0.496398</td>\n",
" <td>1</td>\n",
" <td>1.041786e+06</td>\n",
" <td>-0.611150</td>\n",
" <td>-0.405754</td>\n",
" <td>1783.177886</td>\n",
" <td>0.020107</td>\n",
" <td>5412.625995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1967</td>\n",
" <td>0.401090</td>\n",
" <td>0</td>\n",
" <td>2.284852e+05</td>\n",
" <td>-0.196838</td>\n",
" <td>-0.301843</td>\n",
" <td>527.226453</td>\n",
" <td>0.034232</td>\n",
" <td>3316.301112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>151</td>\n",
" <td>0.499611</td>\n",
" <td>1</td>\n",
" <td>3.597816e+05</td>\n",
" <td>0.241001</td>\n",
" <td>-0.153348</td>\n",
" <td>524.060903</td>\n",
" <td>0.011216</td>\n",
" <td>4010.773810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5</td>\n",
" <td>0.500247</td>\n",
" <td>1</td>\n",
" <td>5.324209e+06</td>\n",
" <td>-0.130207</td>\n",
" <td>-0.202091</td>\n",
" <td>2541.108767</td>\n",
" <td>0.006781</td>\n",
" <td>7003.905325</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RMS ZCR audio bandwidth nwpd rse \\\n",
"0 120 0.317919 1 2.852637e+05 -1.086645 -0.190269 \n",
"1 2 0.442350 1 6.743143e+05 10.745700 -0.875232 \n",
"2 13 0.464951 1 8.572244e+05 1.387262 -0.322468 \n",
"3 130 0.150850 1 3.938587e+06 0.498670 -0.290313 \n",
"4 42 0.250210 1 6.657314e+05 0.925133 -0.263716 \n",
"5 93 0.481068 1 7.152688e+05 -0.090272 -0.184233 \n",
"6 12 0.496398 1 1.041786e+06 -0.611150 -0.405754 \n",
"7 1967 0.401090 0 2.284852e+05 -0.196838 -0.301843 \n",
"8 151 0.499611 1 3.597816e+05 0.241001 -0.153348 \n",
"9 5 0.500247 1 5.324209e+06 -0.130207 -0.202091 \n",
"\n",
" spectral_centroid spectral_flux spectral_rolloff \n",
"0 535.891491 0.039433 4020.624371 \n",
"1 840.023296 0.061607 3833.170732 \n",
"2 572.973659 0.018836 4938.532110 \n",
"3 3448.585720 0.010311 6988.571429 \n",
"4 745.102831 0.014008 4590.849315 \n",
"5 738.235253 0.022868 4529.035025 \n",
"6 1783.177886 0.020107 5412.625995 \n",
"7 527.226453 0.034232 3316.301112 \n",
"8 524.060903 0.011216 4010.773810 \n",
"9 2541.108767 0.006781 7003.905325 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"voice_noise_df[\"audio\"] = voice_noise_df[\"audio\"].astype('category')\n",
"voice_noise_df[\"audio\"] = voice_noise_df[\"audio\"].cat.codes\n",
"voice_noise_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([132, 229]), array([4, 4]))\n",
"(1356,)\n",
"(1354, 8)\n",
"(array([], dtype=int64), array([], dtype=int64))\n",
"(1083, 8)\n",
"(array([], dtype=int64), array([], dtype=int64))\n",
"(array([], dtype=int64), array([], dtype=int64))\n"
]
}
],
"source": [
"TRAIN_TEST_PERCENTAGE = 0.80\n",
"data = voice_noise_df.loc[:,voice_noise_df.columns != \"audio\"].as_matrix()\n",
"labels = voice_noise_df[\"audio\"].as_matrix()\n",
"\n",
"full_data = np.hstack((data,np.expand_dims(labels,axis = 1)))\n",
"random.shuffle(full_data)\n",
"data = full_data[:,0:-1]\n",
"labels = full_data[:,-1]\n",
"\n",
"nan_indices = np.where(np.isnan(data))\n",
"print(nan_indices)\n",
"all_indices_ = np.ones(len(data),dtype = \"bool\")\n",
"print(all_indices_.shape)\n",
"all_indices_[nan_indices[0]] = False\n",
"data_ = copy.deepcopy(data[all_indices_,:])\n",
"labels_ = copy.deepcopy(labels[all_indices_])\n",
"print(data_.shape)\n",
"print(np.where(np.isnan(data_)))\n",
"\n",
"index_ = int(len(data_)*TRAIN_TEST_PERCENTAGE)\n",
"train_data = data_[0:index_,:]\n",
"train_labels = labels_[0:index_]\n",
"test_data = data_[index_:,:]\n",
"test_labels = labels_[index_:]\n",
"\n",
"print(train_data.shape)\n",
"\n",
"print(np.where(np.isnan(train_data)))\n",
"\n",
"print(np.where(np.isnan(test_data)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"classifier = tree.DecisionTreeClassifier(max_depth = 3)\n",
"classifier = classifier.fit(train_data,train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.97416973\n"
]
}
],
"source": [
"prediction = classifier.predict(test_data)\n",
"print(np.mean(np.equal(prediction,test_labels).astype(np.float32)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For testing new audio"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"TEST_AUDIO_FOLDER = \"/Users/siva/Documents/speaker_recognition/VOD/testwav/\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def create_dataset(ROOT_FOLDER,WINDOW_LENGTH = 5,FRAME_LENGTH = 25):\n",
" os.chdir(ROOT_FOLDER)\n",
" all_audio = os.listdir()\n",
" dataset_dict = {\"ZCR\":[],\"RMS\":[],\"spectral_flux\":[],\\\n",
" \"spectral_centroid\":[],\"spectral_rolloff\":[],\\\n",
" \"bandwidth\":[],\"nwpd\":[],\"rse\":[]}\n",
" for audio in all_audio:\n",
" print(\"****************************\")\n",
" print(\"reading:\",audio)\n",
" sampling_rate,sig = wav.read(ROOT_FOLDER+audio)\n",
" print(\"sampling rate:\",sampling_rate,\"signal length\",len(sig))\n",
" index = 0\n",
" while index+(sampling_rate*WINDOW_LENGTH) < len(sig):\n",
" sample = sig[index:(index+(sampling_rate*WINDOW_LENGTH))]\n",
" ef = extract_features(sample,FRAME_LENGTH,sampling_rate)\n",
" ZCR,RMS,sf,sr,sc,bd,nwpd,rse = ef.return_()\n",
" dataset_dict[\"ZCR\"].append(ZCR)\n",
" dataset_dict[\"RMS\"].append(RMS)\n",
" dataset_dict[\"spectral_flux\"].append(np.mean(sf))\n",
" dataset_dict[\"spectral_centroid\"].append(np.mean(sc))\n",
" dataset_dict[\"spectral_rolloff\"].append(np.mean(sr))\n",
" dataset_dict[\"bandwidth\"].append(np.mean(bd))\n",
" dataset_dict[\"nwpd\"].append(np.mean(nwpd))\n",
" dataset_dict[\"rse\"].append(np.mean(rse))\n",
" index += sampling_rate*WINDOW_LENGTH\n",
" values = dataset_dict.values()\n",
" print([len(e) for e in values])\n",
" print(\"finished\")\n",
" return dataset_dict"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************\n",
"reading: audiotest08-06-2018-17-01-26.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-17-01-43.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-17-01-56.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-17-02-11.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-16-49-40.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-17-01-12.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-13-12-39.wav\n",
"sampling rate: 16000 signal length 84480\n",
"****************************\n",
"reading: audiotest08-06-2018-17-02-44.wav\n",
"sampling rate: 16000 signal length 84480\n",
"[8, 8, 8, 8, 8, 8, 8, 8]\n",
"finished\n"
]
}
],
"source": [
"features_test_dict = create_dataset(TEST_AUDIO_FOLDER)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RMS</th>\n",
" <th>ZCR</th>\n",
" <th>bandwidth</th>\n",
" <th>nwpd</th>\n",
" <th>rse</th>\n",
" <th>spectral_centroid</th>\n",
" <th>spectral_flux</th>\n",
" <th>spectral_rolloff</th>\n",
" </tr>\n",
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" <th>0</th>\n",
" <td>235</td>\n",
" <td>0.148002</td>\n",
" <td>1.800357e+06</td>\n",
" <td>0.323637</td>\n",
" <td>-0.258980</td>\n",
" <td>1043.434918</td>\n",
" <td>0.021088</td>\n",
" <td>6197.853916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>347</td>\n",
" <td>0.239553</td>\n",
" <td>3.614656e+06</td>\n",
" <td>1.323582</td>\n",
" <td>-0.249343</td>\n",
" <td>1700.323351</td>\n",
" <td>0.008497</td>\n",
" <td>6469.879518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>271</td>\n",
" <td>0.130902</td>\n",
" <td>1.469745e+06</td>\n",
" <td>1.151613</td>\n",
" <td>-0.253432</td>\n",
" <td>988.019173</td>\n",
" <td>0.014795</td>\n",
" <td>5685.115462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>140</td>\n",
" <td>0.125277</td>\n",
" <td>1.089120e+06</td>\n",
" <td>-0.566549</td>\n",
" <td>-0.337949</td>\n",
" <td>926.835474</td>\n",
" <td>0.019217</td>\n",
" <td>5463.227912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>270</td>\n",
" <td>0.093726</td>\n",
" <td>1.016863e+06</td>\n",
" <td>3.289868</td>\n",
" <td>-0.305840</td>\n",
" <td>786.064634</td>\n",
" <td>0.027444</td>\n",
" <td>5227.158635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>242</td>\n",
" <td>0.179977</td>\n",
" <td>2.751192e+06</td>\n",
" <td>0.520077</td>\n",
" <td>-0.235335</td>\n",
" <td>1297.176396</td>\n",
" <td>0.016272</td>\n",
" <td>6467.871486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>31</td>\n",
" <td>0.085739</td>\n",
" <td>1.137872e+06</td>\n",
" <td>0.515145</td>\n",
" <td>-0.236153</td>\n",
" <td>641.419533</td>\n",
" <td>0.026151</td>\n",
" <td>4864.646084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>187</td>\n",
" <td>0.107876</td>\n",
" <td>6.686752e+05</td>\n",
" <td>0.009063</td>\n",
" <td>-0.304043</td>\n",
" <td>802.714148</td>\n",
" <td>0.020602</td>\n",
" <td>5183.985944</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RMS ZCR bandwidth nwpd rse spectral_centroid \\\n",
"0 235 0.148002 1.800357e+06 0.323637 -0.258980 1043.434918 \n",
"1 347 0.239553 3.614656e+06 1.323582 -0.249343 1700.323351 \n",
"2 271 0.130902 1.469745e+06 1.151613 -0.253432 988.019173 \n",
"3 140 0.125277 1.089120e+06 -0.566549 -0.337949 926.835474 \n",
"4 270 0.093726 1.016863e+06 3.289868 -0.305840 786.064634 \n",
"5 242 0.179977 2.751192e+06 0.520077 -0.235335 1297.176396 \n",
"6 31 0.085739 1.137872e+06 0.515145 -0.236153 641.419533 \n",
"7 187 0.107876 6.686752e+05 0.009063 -0.304043 802.714148 \n",
"\n",
" spectral_flux spectral_rolloff \n",
"0 0.021088 6197.853916 \n",
"1 0.008497 6469.879518 \n",
"2 0.014795 5685.115462 \n",
"3 0.019217 5463.227912 \n",
"4 0.027444 5227.158635 \n",
"5 0.016272 6467.871486 \n",
"6 0.026151 4864.646084 \n",
"7 0.020602 5183.985944 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.DataFrame.from_dict(features_test_dict)\n",
"test_df.replace([np.inf,-np.inf,np.nan],0)\n",
"test_df.dropna()\n",
"test_df_data = test_df.as_matrix()\n",
"test_df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Voice and 0 - Noise"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1. 1. 1. 1. 1. 1. 1.]\n"
]
}
],
"source": [
"test_predictions = classifier.predict(test_df_data)\n",
"print(test_predictions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
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