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{
"cell_type": "markdown",
"source": [
"# Aggregate MFCC Features\n",
"\n",
"Aggregate from n rows par file to 1 (calculate min, max, etc. for each feature)."
],
"metadata": {
"collapsed": false
}
},
"metadata": {
"collapsed": true
},
"outputs": [],
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"source": [
"import pandas as pd\n",
"from definitions import BASE_PATH"
]
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"# inputs\n",
"\n",
"INPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"input\"\n",
"assert INPUT_PATH.exists() and INPUT_PATH.is_dir()\n",
"\n",
"raw_features = pd.read_csv(INPUT_PATH / \"raw_features.csv\", index_col=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 24,
"outputs": [
{
"data": {
"text/plain": " filename label 0_min 0_max 0_mean \\\n0 classical_1.mp3 classical -530.78436 -163.308350 -302.203167 \n1 classical_10.mp3 classical -562.85785 -96.164795 -219.259016 \n2 classical_100.mp3 classical -536.23737 -61.608826 -177.804114 \n3 classical_11.mp3 classical -536.45746 -120.429665 -222.126303 \n4 classical_12.mp3 classical -562.67523 -148.133560 -270.975406 \n.. ... ... ... ... ... \n395 rock_95.mp3 rock -553.11010 -5.218835 -193.506047 \n396 rock_96.mp3 rock -541.23600 27.163334 -119.113996 \n397 rock_97.mp3 rock -518.49500 58.526745 -66.267744 \n398 rock_98.mp3 rock -518.64307 53.555115 -45.734517 \n399 rock_99.mp3 rock -544.70310 75.612130 -49.380943 \n\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n0 51.142183 -0.468374 0.000000 178.75162 111.332342 ... -44.098070 \n1 53.561838 -0.772320 0.029056 259.63270 215.094182 ... -27.458416 \n2 83.381622 -2.587179 0.000000 190.47589 112.471713 ... -27.335688 \n3 76.246992 -2.402418 0.000000 159.42575 99.853645 ... -31.774948 \n4 52.191182 -0.366586 0.000000 194.26416 148.226647 ... -44.843810 \n.. ... ... ... ... ... ... ... \n395 76.869437 -0.201055 -89.948746 201.18045 111.724191 ... -27.043941 \n396 58.420684 -0.957699 -7.415961 210.49246 125.453699 ... -37.584858 \n397 65.635619 -0.898026 -58.824410 175.20135 99.288265 ... -29.620445 \n398 52.444200 -1.705641 0.000000 187.04274 96.440874 ... -26.967848 \n399 54.045627 -0.863093 -32.930653 191.73538 93.971242 ... -21.929403 \n\n 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n0 47.308060 -3.713503 16.553984 0.230691 -46.794480 49.352516 -2.282116 \n1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397880 -1.494916 \n3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n.. ... ... ... ... ... ... ... \n395 22.451445 -7.234634 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n396 28.087936 -9.704238 8.447620 0.112760 -38.147890 21.814402 -8.249507 \n397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410654 -3.356614 \n398 8.714737 -9.511491 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n399 17.050608 -5.296691 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n\n 39_std 39_skew \n0 15.285639 0.171462 \n1 10.477735 -0.185771 \n2 10.917299 0.020985 \n3 10.125545 0.595763 \n4 11.160392 0.503120 \n.. ... ... \n395 6.687984 0.238807 \n396 7.807756 0.071968 \n397 8.170526 0.160330 \n398 5.051498 -0.258407 \n399 6.571660 0.384794 \n\n[400 rows x 202 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>classical</td>\n <td>-530.78436</td>\n <td>-163.308350</td>\n <td>-302.203167</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.75162</td>\n <td>111.332342</td>\n <td>...</td>\n <td>-44.098070</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794480</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.85785</td>\n <td>-96.164795</td>\n <td>-219.259016</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.63270</td>\n <td>215.094182</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.23737</td>\n <td>-61.608826</td>\n <td>-177.804114</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.47589</td>\n <td>112.471713</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397880</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020985</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.45746</td>\n <td>-120.429665</td>\n <td>-222.126303</td>\n <td>76.246992</td>\n <td>-2.402418</td>\n <td>0.000000</td>\n <td>159.42575</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.67523</td>\n <td>-148.133560</td>\n <td>-270.975406</td>\n <td>52.191182</td>\n <td>-0.366586</td>\n <td>0.000000</td>\n <td>194.26416</td>\n <td>148.226647</td>\n <td>...</td>\n <td>-44.843810</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.11010</td>\n <td>-5.218835</td>\n <td>-193.506047</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.18045</td>\n <td>111.724191</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234634</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.23600</td>\n <td>27.163334</td>\n <td>-119.113996</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.49246</td>\n <td>125.453699</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147890</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.49500</td>\n <td>58.526745</td>\n <td>-66.267744</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824410</td>\n <td>175.20135</td>\n <td>99.288265</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722825</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356614</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.64307</td>\n <td>53.555115</td>\n <td>-45.734517</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.04274</td>\n <td>96.440874</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511491</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.70310</td>\n <td>75.612130</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.73538</td>\n <td>93.971242</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296691</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meta_columns = [\"sample\", \"filename\", \"label\"]\n",
"mfcc_aggregated = raw_features\\\n",
" .drop(meta_columns, axis=1, errors='ignore')\\\n",
" .groupby(raw_features.filename).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
"\n",
"mfcc_meta = pd.DataFrame(raw_features['label'].groupby(raw_features.filename).last())\n",
"mfcc_meta.columns = pd.MultiIndex.from_arrays([['label'], ['']]) # needed for merge\n",
"mfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
"\n",
"# reduce multi index to single index\n",
"one_level_cols = ['_'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\n",
"one_level_cols.insert(0, \"label\")\n",
"\n",
"mfcc_merged.columns = pd.Index(one_level_cols)\n",
"mfcc_merged = mfcc_merged.reset_index()\n",
"mfcc_merged"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 25,
"outputs": [],
"source": [
"# outputs\n",
"OUTPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"output\"\n",
"output = mfcc_merged\n",
"output.to_csv(OUTPUT_PATH / \"output.csv\", index=False)\n",
"output.to_pickle(OUTPUT_PATH / \"output.pickle\")"
],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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},
"nbformat": 4,
"nbformat_minor": 0
}