Repository: danz1ka19/Music-Emotion-Recognition
Branch: master
Commit: 32107793ce93
Files: 13
Total size: 81.9 KB
Directory structure:
gitextract_nu0kbpz2/
├── Emotion_features.csv
├── Feature-Extraction.py
├── README.md
└── SourceCode/
├── Emotion-Recognition-RandomSeed.py
├── Emotion-Recognition.py
├── Feature-Extraction.py
├── HyperparamaterTuning.py
├── ScatterPlotDistribution.py
├── ScatterPlotNormalizedDistribution.py
├── SingleFeaturekNN.py
├── ViolinAndStripSubplot.py
├── ViolinStripAndMixPlot.py
└── Visualization.py
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FILE CONTENTS
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FILE: Emotion_features.csv
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id,song_name,class,label,tempo,total_beats,average_beats,chroma_stft_mean,chroma_stft_std,chroma_stft_var,chroma_cq_mean,chroma_cq_std,chroma_cq_var,chroma_cens_mean,chroma_cens_std,chroma_cens_var,melspectrogram_mean,melspectrogram_std,melspectrogram_var,mfcc_mean,mfcc_std,mfcc_var,mfcc_delta_mean,mfcc_delta_std,mfcc_delta_var,rmse_mean,rmse_std,rmse_var,cent_mean,cent_std,cent_var,spec_bw_mean,spec_bw_std,spec_bw_var,contrast_mean,contrast_std,contrast_var,rolloff_mean,rolloff_std,rolloff_var,poly_mean,poly_std,poly_var,tonnetz_mean,tonnetz_std,tonnetz_var,zcr_mean,zcr_std,zcr_var,harm_mean,harm_std,harm_var,perc_mean,perc_std,perc_var,frame_mean,frame_std,frame_var
1,30 seconds to Mars - Night of the Hunter.mp3,relax,3,117.4538352,139925,1283.715596,0.422269364,0.293325614,0.086039916,0.527779494,0.263042667,0.069191445,0.261495642,0.117303546,0.013760122,8.27174432,42.23890769,1784.125323,-0.669535765,36.23852434,1313.230646,0.012002837,2.737308806,7.492859498,4.831246853,2.73298049,7.469182968,2749.820569,982.5022573,965310.6856,2500.560413,584.3546489,341470.3556,18.90063587,8.135854586,66.19212985,5676.132344,1775.02147,3150701.22,2.694325741,3.498507562,12.23955516,0.001847167,0.055645316,0.003096401,0.145777463,0.088269907,0.007791576,1.36E-06,0.131658405,0.017333934,0.000141969,0.095362484,0.009094004,4.221387755,1.601395952,2.564468994
2,Absolutely - Story of A Girl.mp3,sad,1,129.1992188,159877,1268.865079,0.425320782,0.289704251,0.083928553,0.488411633,0.275777219,0.076053074,0.259517351,0.126428153,0.015984078,10.80452964,52.99969193,2808.967345,2.324514489,23.12582986,534.8040068,0.012688937,2.348498004,5.515442877,6.097115994,1.973801136,3.895890951,3355.923318,825.28016,681087.3424,2839.385726,282.88856,80025.93736,20.00986003,6.837942054,46.75745153,6770.39656,1060.310894,1124259.193,3.068247328,3.49566783,12.21969358,0.007194285,0.071367416,0.005093308,0.174273512,0.092381368,0.008534317,-3.00E-06,0.169203728,0.028629901,5.56E-06,0.099144682,0.009829667,3.24614966,1.885734819,3.555995808
3,Ace - Futureland.mp3,happy,2,99.38401442,121594,1253.546392,0.487376803,0.268670374,0.07218377,0.44053569,0.303527672,0.092129048,0.238127465,0.163182854,0.026628644,16.9294511,91.41765116,8357.186944,7.213594738,25.7773076,664.4695872,0.00664681,1.732102757,3.00017996,7.45171833,2.472208023,6.111813068,2884.310298,1000.080209,1000160.423,2778.911699,549.9270779,302419.791,18.09058591,6.242264389,38.9658647,6208.412459,2138.990993,4575282.467,3.751397711,4.212932682,17.74880178,-0.019074338,0.083522546,0.006976016,0.136353862,0.078736655,0.006199461,1.87E-05,0.206915513,0.042814031,-7.70E-05,0.115424842,0.013322894,3.989188209,1.766115307,3.119163277
4,Alan Walker - Faded.mp3,sad,1,89.10290948,113611,1291.034091,0.292934478,0.29569479,0.087435409,0.406712844,0.288187825,0.083052223,0.226562938,0.178892617,0.032002568,9.843200113,75.09972899,5639.969295,2.453629006,45.42074375,2063.043963,0.005337784,2.206856993,4.870217787,5.226909637,2.562407494,6.565932274,1927.612816,1035.043044,1071314.103,2174.151882,835.8544316,698652.6308,21.74016032,5.647970149,31.8995668,4051.608834,2482.790129,6164246.825,2.098193939,2.550848768,6.506829439,-0.002768046,0.092026768,0.008468926,0.084333957,0.049829604,0.002482989,-2.03E-05,0.184177279,0.033921268,-3.26E-06,0.055471476,0.003077085,3.423782313,1.922332842,3.695363555
5,Alan Walker - Force [Instrumental].mp3,relax,3,198.7680288,252770,1404.277778,0.286669482,0.300048682,0.090029212,0.37287328,0.290104642,0.084160703,0.217227015,0.190120375,0.036145757,5.886977146,72.02582901,5187.720045,-0.764524421,45.97753844,2113.934041,0.009725682,2.007709816,4.030898707,3.845882177,2.61906004,6.859476089,2545.374828,1062.659044,1129244.244,2371.939368,908.3368211,825075.7805,21.94492058,6.550948614,42.91492774,5321.330323,2555.896372,6532606.266,1.512831214,1.997955863,3.99182763,-0.006246197,0.091778097,0.008423219,0.117507415,0.054558505,0.00297663,1.40E-05,0.147810981,0.021848086,0.000306882,0.048204396,0.002323664,9.637442177,7.344525154,53.94204973
6,All American Rejects - Stab my back.mp3,angry,4,135.9991776,173919,1288.288889,0.402927771,0.293648941,0.086229701,0.511188687,0.267794641,0.07171397,0.261786125,0.121660832,0.014801358,16.47518906,86.45292344,7474.107971,6.878092703,23.32246751,543.9374908,0.005724838,2.711800544,7.353862193,7.291646957,2.602593184,6.773491859,2794.75673,680.0011967,462401.6275,2697.979678,303.4514805,92082.80102,19.93235094,6.390375704,40.83690164,5915.443397,1193.495011,1424430.34,3.739061441,4.257751352,18.12844658,0.009007424,0.059399822,0.003528339,0.132281324,0.057834691,0.003344851,-2.44E-06,0.196422249,0.038581699,-0.000103147,0.135027885,0.018232528,2.917587302,1.705446393,2.908547399
7,Apparat - You Dont Know Me.mp3,relax,3,161.4990234,204713,1271.509317,0.291769794,0.303724422,0.092248524,0.439677776,0.283746334,0.080511982,0.237816404,0.163635849,0.026776691,6.974083403,66.83671905,4467.147014,-3.754776661,48.89451485,2390.673583,0.012782354,1.440528756,2.075123096,4.111234188,2.577727079,6.644677162,1722.14567,375.4770208,140982.9932,2280.988092,408.1874201,166616.9699,23.76722385,8.232435012,67.77298623,3775.777168,1272.897506,1620268.061,1.534552304,2.227213372,4.960479402,0.007724619,0.076847818,0.005905587,0.062141537,0.025718884,0.000661461,-1.92E-05,0.157182351,0.024706293,-1.63E-05,0.039745864,0.001579734,1.37229932,0.69298631,0.480230026
8,B.O.B - Soo Good.mp3,happy,2,86.1328125,108756,1279.482353,0.421205622,0.294016598,0.08644576,0.507742092,0.279095371,0.077894226,0.254880185,0.13553385,0.018369425,20.03077151,147.5136999,21760.29165,4.95562076,29.67170653,880.4101682,0.013385615,2.746273852,7.542020071,7.625406742,3.578936338,12.80878448,2708.439843,953.862517,909853.7014,2623.42318,734.9657335,540174.6295,19.42979135,6.589739629,43.42466838,5880.66444,2082.871221,4338352.525,3.594682842,4.352430408,18.94365045,-0.00407983,0.070444611,0.004962443,0.11792748,0.063363736,0.004014963,-2.28E-06,0.199292004,0.039717302,-0.00010311,0.165504798,0.027391838,5.263963719,2.912277593,8.481360778
9,Billy Talent - Fallen Leaves.mp3,happy,2,123.046875,106506,1054.514851,0.408958424,0.289546437,0.083837139,0.522171574,0.279307258,0.078012544,0.259815229,0.125814864,0.01582938,13.57571267,81.45232998,6634.482058,4.532875529,26.8421505,720.5010435,0.010696634,2.909786962,8.466860165,6.407421589,2.945688963,8.677083015,2862.122038,629.6575466,396468.626,2583.918199,409.17947,167427.8387,19.68229392,7.402166278,54.7920656,5834.710552,1155.844456,1335976.406,3.299955133,3.889061712,15.124801,0.016530577,0.066490072,0.00442093,0.146345676,0.058125786,0.003378607,-1.90E-05,0.177508309,0.031509198,-0.000155616,0.122583307,0.015026667,2.402104308,1.407299272,1.980491241
10,Billy Talent - Red Flag.mp3,happy,2,92.28515625,102169,1201.988235,0.472775697,0.291974877,0.085249329,0.575409757,0.277486308,0.076998651,0.266335384,0.111349883,0.012398796,16.15352587,109.9906259,12097.93778,6.342557104,22.41891883,502.6079216,0.010971336,2.849928707,8.122093634,7.119541168,2.820424318,7.95479393,3066.501669,790.8160532,625390.03,2778.984692,298.3711979,89025.37171,18.74509054,6.857769624,47.02900422,6338.161674,1135.978382,1290446.884,3.510186788,4.073010511,16.58941462,0.005264146,0.06428996,0.004133199,0.158338045,0.079177701,0.006269108,9.09E-06,0.163210422,0.02663764,0.000865846,0.161110818,0.025956694,1.733369615,1.053179817,1.109187727
11,Billy Talent - Viking Death March.mp3,angry,4,143.5546875,183008,1279.776224,0.466557562,0.282420875,0.079761551,0.584324635,0.247357228,0.061185598,0.268748724,0.10539192,0.011107457,17.10669841,93.67338601,8774.703246,6.364959232,26.87810473,722.4325138,0.013899837,2.4043216,5.780762356,7.347826958,2.754977942,7.589903355,2586.263822,584.5253864,341669.9274,2423.44088,399.0401254,159233.0217,19.00914569,7.564149979,57.21636491,5332.755254,1081.926165,1170564.226,3.989431764,4.433234437,19.65356757,0.004083563,0.050217035,0.002521751,0.13435633,0.05160216,0.002662783,-9.60E-06,0.182128429,0.033170767,-0.000246938,0.139302582,0.01940521,6.849886621,3.006898956,9.041441332
12,Black Eyed Peas - Lets Get It Started.mp3,happy,2,103.359375,129354,1306.606061,0.402474583,0.312443126,0.097620707,0.466007204,0.296977642,0.08819572,0.260350916,0.124702582,0.015550734,12.99421651,101.599269,10322.41146,0.792898904,41.49389062,1721.742959,0.011871489,3.554022516,12.63107604,5.909911633,3.422062159,11.7105093,2644.788328,1153.020189,1329455.556,2635.541868,503.5351806,253547.6781,19.97006123,6.920022783,47.88671532,5501.200068,2037.249226,4150384.408,2.604742209,3.678209832,13.52922757,-0.003095443,0.071901439,0.005169817,0.116087733,0.096375337,0.009288206,2.30E-06,0.14649725,0.021461442,-6.78E-05,0.144702166,0.020938715,4.447782313,2.376100069,5.64585154
13,Blue Stahli - Ultranumb.mp3,angry,4,123.046875,147270,1248.050847,0.535376136,0.263895309,0.069640734,0.554991266,0.262331103,0.068817608,0.265375858,0.113617724,0.012908987,10.95176986,71.0129774,5042.84296,3.435042903,31.84942408,1014.385814,0.013709524,2.150124998,4.623037509,5.561200142,3.0498631,9.301665306,2938.003397,690.8869368,477324.7595,2600.273189,502.5470182,252553.5055,17.93798924,7.466678433,55.75128682,5979.384677,1419.856955,2015993.771,2.958585796,3.678379398,13.530475,-0.006745901,0.057526558,0.003309305,0.146447716,0.065800428,0.004329696,0.000169703,0.166842565,0.02783644,0.001528302,0.095579937,0.009135525,2.813097506,1.338913305,1.792688838
14,Blue Stahli vs Celldweller - Frozen.mp3,relax,3,129.1992188,159858,1258.724409,0.413806951,0.315671369,0.099648413,0.576091406,0.265046685,0.070249745,0.272654286,0.094831291,0.008992974,2.695430781,22.19727107,492.7188431,-6.422768463,51.17730624,2619.116674,0.00982906,3.39416723,11.52037119,2.597597361,1.560394883,2.434832096,2041.541831,941.4555868,886338.6219,2102.412011,762.3423281,581165.8253,18.41992234,7.353510188,54.07411208,4276.482474,2096.566806,4395592.37,1.235656873,1.626491587,2.645474881,0.001037427,0.051274494,0.002629074,0.089129091,0.051724523,0.002675426,0.000409699,0.061693363,0.003806071,0.000708763,0.066476807,0.004419166,2.972154195,1.773205029,3.144256076
15,Bonnie Tyler - Total Eclipse of the Heart.mp3,sad,1,129.1992188,162877,1282.496063,0.271930177,0.302700028,0.091627307,0.421102029,0.282977046,0.080076008,0.23802667,0.163329843,0.026676638,2.321361634,22.96136834,527.224436,-9.634292097,58.87850751,3466.678646,0.011873367,2.576473109,6.638213679,2.272249937,1.917987227,3.678674936,2170.832467,611.3881309,373795.4466,1953.28201,406.1581802,164964.4673,23.70792469,6.938499402,48.14277395,3939.21368,1221.556987,1492201.472,0.998802059,1.576373648,2.484953877,-0.002788675,0.080037797,0.006406049,0.118865872,0.050058968,0.0025059,4.05E-06,0.083132863,0.006911073,-2.22E-05,0.044358771,0.001967701,4.288725624,2.341742499,5.483757933
16,Breaking Benjamin - Breath.mp3,happy,2,92.28515625,113335,1259.277778,0.396369489,0.284254401,0.080800565,0.455142365,0.278811698,0.077735963,0.242566925,0.156507572,0.02449462,14.01458824,73.58552948,5414.830149,5.870747545,31.29247474,979.2189753,0.005343709,2.13405156,4.554176062,6.657815456,2.263454914,5.123228073,2297.396476,706.1790814,498688.895,2435.428403,488.8099214,238935.1392,20.47385432,6.463104091,41.77171449,4924.782721,1564.307282,2447057.274,3.217956805,3.651106068,13.33057552,-0.006948982,0.078250439,0.006123131,0.098866749,0.044492392,0.001979573,2.30E-06,0.197886735,0.03915916,-3.08E-05,0.098269477,0.00965689,2.775945578,1.819598304,3.310937988
17,Breaking Benjamin - I Will Not Bow.mp3,angry,4,107.6660156,131015,1259.759615,0.454749297,0.276858766,0.076650777,0.519307059,0.279829394,0.07830449,0.256522975,0.132398251,0.017529297,22.7630754,130.2865158,16974.57619,6.011973516,30.30760478,918.5509072,0.012180728,2.02058726,4.082772874,8.239292145,3.566739082,12.72162819,2736.437967,700.7225646,491012.1125,2584.263729,446.1937209,199088.8366,19.83836196,6.974203171,48.63950988,5698.290543,1356.499374,1840090.551,3.932229778,4.604011199,21.19691912,0.013427586,0.074149712,0.00549818,0.126941976,0.0630339,0.003973273,6.51E-06,0.242872566,0.058987081,2.51E-05,0.129716977,0.016826494,6.032544218,2.300540956,5.292488691
18,Breaking Benjamin - Into the Nothing.mp3,angry,4,95.703125,117594,1264.451613,0.467404991,0.276425128,0.076410851,0.467367862,0.277421652,0.076962773,0.249772923,0.144730163,0.02094682,25.05559995,116.2035881,13503.2739,8.708448371,23.51670469,553.0353997,0.013760307,2.141404861,4.585614781,9.290811539,1.329018354,1.76628983,2576.623891,758.5768228,575438.7961,2630.615574,378.470166,143239.6666,19.63944687,7.326305717,53.67475546,5630.645119,1464.339782,2144290.997,4.380989291,4.554014299,20.73904624,0.000707387,0.073122949,0.005346966,0.107878221,0.055249994,0.003052562,8.38E-06,0.26386112,0.069622695,0.000203638,0.125312135,0.015703131,5.979138322,3.538811743,12.52318855
19,Breaking Benjamin - The Diary of Jane.mp3,sad,1,83.35433468,97869,1223.3625,0.448234046,0.290374201,0.084317177,0.496579418,0.28327459,0.080244493,0.247830579,0.148031543,0.021913338,3.136189496,16.41105926,269.3228661,0.100834315,40.2192699,1617.589671,0.012330392,2.012192377,4.048918163,3.206317663,1.156541944,1.337589145,2559.494232,886.7918066,786399.7083,2413.081282,649.974529,422466.8884,20.57333583,8.058157569,64.93390341,5248.909927,1843.138435,3397159.29,1.680687013,1.964607174,3.859681347,-0.028696992,0.074541635,0.005556455,0.131191384,0.061219679,0.003747849,-6.34E-07,0.088962898,0.007914397,3.73E-05,0.054132815,0.002930362,3.646693878,1.759200449,3.094786221
20,Buckcherry vs The Prodigy - Crazy Bitch vs Girls.mp3,happy,2,123.046875,138193,1191.318966,0.484846595,0.298187343,0.088915691,0.528964057,0.282039846,0.079546475,0.262262598,0.120630275,0.014551663,22.82086316,178.0462646,31700.47235,5.838676459,31.76448503,1008.98251,0.004356527,4.43373736,19.65802698,8.748059273,3.853826761,14.85198116,2611.001127,891.9444653,795564.9292,2558.014327,489.432179,239543.8579,18.53984543,6.834016124,46.70377639,5458.266995,1732.339606,3001000.511,4.057726691,4.863786848,23.65642251,0.007551954,0.052077383,0.002712054,0.108621792,0.071305098,0.005084417,0.069523379,0.194745436,0.037925787,0.009843239,0.186252549,0.034690011,2.142040816,1.142904826,1.306231441
21,Celldweller - Louder than Words.mp3,relax,3,129.1992188,163073,1274.007813,0.497176541,0.277366145,0.076931978,0.459451068,0.282600434,0.079863006,0.244975362,0.1527102,0.023320405,14.16715445,82.89102642,6870.92226,7.249865721,32.62323038,1064.27516,0.013239201,2.362641172,5.582073307,6.733174801,2.144689322,4.599691868,2222.871484,872.0314977,760438.933,2365.303478,595.8328373,355016.77,18.73737298,7.624103453,58.12695347,4788.258546,1821.102116,3316412.915,3.23242565,3.741318874,13.99746691,0.01230219,0.080442594,0.006471011,0.088543493,0.058361267,0.003406037,7.76E-05,0.188289493,0.035452932,0.000102256,0.10894613,0.011869259,4.06000907,2.704972985,7.316878851
22,Dangerkids - Cut Me Out.mp3,happy,2,135.9991776,175185,1269.456522,0.3958758,0.287968708,0.082925977,0.413221524,0.279513041,0.07812754,0.236254215,0.16588333,0.027517279,16.43499268,86.67369702,7512.329755,6.160606666,33.09131347,1095.035027,0.011221741,2.096554143,4.395539274,7.195658684,2.734534979,7.477682114,2433.756941,900.8447633,811521.2876,2370.158518,718.7005028,516530.4127,19.40367777,6.811914725,46.40218222,5082.994264,1952.464154,3812116.271,3.758330857,4.382051782,19.20237782,-0.008157792,0.086879214,0.007547998,0.110705113,0.052257411,0.002730837,5.05E-06,0.208215848,0.043353837,0.000171728,0.111212038,0.012368117,2.129269841,1.171310353,1.371967943
23,Data - One in A Million.mp3,happy,2,123.046875,153383,1369.491071,0.386756091,0.306088936,0.093690437,0.563429303,0.266463113,0.071002591,0.264697406,0.115189482,0.013268617,21.63702725,141.882844,20130.74142,2.7213725,29.09887687,846.7446352,0.011374116,2.613571113,6.830753964,8.071249962,3.845243692,14.78589821,2580.425996,878.7271091,772161.3323,2501.706637,385.0069165,148230.3257,19.56973134,6.529178752,42.63017518,5319.538667,1592.881162,2537270.395,4.031660511,4.928140297,24.28656678,0.003895271,0.061088764,0.003731837,0.131912657,0.084198664,0.007089415,-7.76E-05,0.201241747,0.040498242,9.13E-05,0.187978268,0.035335828,5.326657596,2.338082253,5.46662862
24,Demi Lovato - Heart Attack.mp3,sad,1,86.1328125,102981,1240.73494,0.328519301,0.313138987,0.098056025,0.465974507,0.298777519,0.089268006,0.252162857,0.140524826,0.019747227,3.165634164,25.71299902,661.1583187,-2.545148752,33.94254424,1152.09631,0.003179118,2.628647993,6.909790272,3.132388115,1.411688805,1.992865443,3169.081882,832.4739737,693012.9169,2819.827353,376.182783,141513.4862,20.32481925,6.407512786,41.0562201,6570.801934,1521.90087,2316182.257,1.372887568,1.641471213,2.694427743,-0.00104019,0.07577384,0.005741675,0.152663665,0.086358972,0.007457872,-2.49E-07,0.087481767,0.00765306,-9.92E-05,0.060471952,0.003656857,1.791419501,1.035939872,1.073171418
25,Escape The Fate - You're So Beautiful.mp3,happy,2,95.703125,122044,1341.142857,0.421849818,0.306647529,0.094032707,0.550773026,0.257694223,0.066406313,0.267766004,0.10786427,0.011634701,10.59940922,57.23674689,3276.045195,4.597677427,24.8762513,618.8278786,0.011206009,2.085952305,4.351197018,6.117223263,1.803445101,3.252414227,3068.835651,616.8225552,380470.0647,2657.643177,251.3394521,63171.52016,19.07042958,6.990074181,48.86113706,6186.387592,962.0405944,925522.1052,3.266151421,3.587866795,12.87278814,0.011968534,0.053901292,0.002905349,0.156901735,0.064431478,0.004151415,2.83E-06,0.155514091,0.024184631,1.07E-06,0.109294571,0.011945303,3.792979592,1.640983536,2.692826965
26,Evanescence - Bring me to Life.mp3,sad,1,95.703125,107784,1298.60241,0.340195852,0.302053439,0.09123628,0.493420379,0.278402829,0.077508135,0.247131189,0.149196208,0.022259509,6.033988021,37.68174366,1419.913805,4.168183867,46.89909685,2199.525285,-0.000221827,1.781880068,3.175096577,4.312673092,1.545769453,2.389403105,1705.036592,908.744262,825816.1337,1994.561724,613.618628,376527.8206,19.9782589,7.375417855,54.39678853,3353.075558,1862.625881,3469375.171,1.890227812,2.345068729,5.499347344,0.011950872,0.058401508,0.003410736,0.078947557,0.062552936,0.00391287,-7.39E-05,0.132372186,0.017522395,-0.00062404,0.05609706,0.00314688,11.03760544,5.605050202,31.41658777
27,Evans Blue- Cold But Im still here.mp3,sad,1,135.9991776,179373,1290.453237,0.433761231,0.279999101,0.078399497,0.479192749,0.280021093,0.078411813,0.250880037,0.142802452,0.02039254,3.142744341,14.34181315,205.6876045,1.069658741,43.62572604,1903.203973,0.008757593,2.286287253,5.227109403,3.181661844,1.008893371,1.017865777,1954.530726,697.8159715,486947.1301,2135.535296,622.1728452,387099.0493,23.03874562,13.71419195,188.0790609,4184.916361,1642.931228,2699223.019,1.711025023,1.93860204,3.758177871,-0.0135201,0.082926099,0.006876738,0.095124897,0.039668243,0.001573569,-0.001536076,0.084541276,0.007147227,-0.003169537,0.051830992,0.002686452,3.145142857,2.093591192,4.38312408
28,Florida - Right Round.mp3,happy,2,123.046875,127201,1156.372727,0.400897081,0.299637596,0.089782689,0.488034057,0.287805823,0.082832192,0.249344854,0.145466413,0.021160477,7.015688709,67.06943446,4498.309039,0.866641315,37.26856338,1388.945816,0.011124304,2.899072092,8.404618994,4.059482098,2.848655462,8.1148386,2700.535256,821.2123686,674389.7544,2657.727288,434.5367614,188822.197,19.64153164,7.640903884,58.38341216,5815.831499,1600.781178,2562500.381,1.926409324,2.747631781,7.549480405,0.007906402,0.069134937,0.00477964,0.12699224,0.067713724,0.004585148,4.52E-05,0.101845041,0.010372412,0.000129788,0.12085668,0.014606337,12.78606803,10.08795272,101.7667901
29,Globus - Take me away.mp3,sad,1,151.9990809,191920,1279.466667,0.288229975,0.309679581,0.095901443,0.379603551,0.293539461,0.086165415,0.215402843,0.192184674,0.036934949,1.067194318,10.70415438,114.5789209,-12.24285386,91.13048051,8304.764477,0.004661522,1.433142239,2.053896679,1.487624168,1.112414122,1.237465262,695.7151403,337.572982,113955.5182,1021.81088,485.4976063,235707.9258,24.37245403,6.548069022,42.87720792,1170.934586,1006.46382,1012969.421,0.456029814,0.663765386,0.440584487,0.005476558,0.08754497,0.007664122,0.031078043,0.0116779,0.000136373,-5.70E-06,0.06254603,0.003912006,-1.30E-06,0.011289089,0.000127444,2.01200907,1.155837822,1.33596107
30,Globus - Thousand Deaths.mp3,sad,1,135.9991776,178652,1258.112676,0.414298481,0.283215475,0.080211005,0.389382051,0.283668387,0.080467754,0.222889189,0.183449565,0.033653743,9.609018462,40.74106775,1659.834601,6.108056515,31.45654496,989.5142208,0.013958731,1.417640777,2.009705372,5.701161385,1.479744792,2.189644575,1989.337314,437.9278757,191780.8243,2089.410023,373.3703169,139405.3936,19.1103805,7.615343232,57.99345254,3987.117557,954.8532387,911744.7074,3.271087252,3.508646364,12.31059931,0.046769898,0.088471723,0.007827246,0.102124666,0.034928811,0.001220022,0.000143525,0.155091599,0.024053404,0.001162905,0.079369351,0.006299493,9.32861678,5.037220502,25.37359038
31,Imagine Dragons - Radioactive.mp3,happy,2,135.9991776,171634,1271.362963,0.365361971,0.297269026,0.088368874,0.488452639,0.268399763,0.072038433,0.253088905,0.138850061,0.01927934,15.97752145,139.1850681,19372.48319,0.941936879,40.01436931,1601.149752,0.013850195,2.368875103,5.611569253,6.028670788,4.282248974,18.33765602,1830.434792,743.156604,552281.7381,2063.199063,476.4348209,226990.1386,20.85932781,7.347844328,53.99081627,3765.685563,1575.051297,2480786.589,2.714162701,3.844959104,14.78371051,-0.001253918,0.067217617,0.004518208,0.081883103,0.050199679,0.002520008,7.33E-06,0.19862777,0.039452989,8.09E-05,0.10788808,0.011639838,4.203972789,2.251371587,5.068674023
32,Imagine Dragons - Warrior.mp3,angry,4,78.30255682,96895,1274.934211,0.349990664,0.285689661,0.081618582,0.506642854,0.26129877,0.068277047,0.257210757,0.131057087,0.01717596,8.244193189,45.07454532,2031.714636,1.908302944,37.44426431,1402.07293,0.005503734,1.767357853,3.123553782,4.815413475,2.383156538,5.679435253,1930.362907,546.0552709,298176.3588,2176.645657,427.6114919,182851.588,20.02704751,6.916516372,47.83819872,3936.630362,1333.65457,1778634.512,2.443521917,3.014163683,9.085182709,0.015831622,0.06479114,0.004197892,0.089114541,0.034039781,0.001158707,1.64E-06,0.149282426,0.022285243,-3.35E-05,0.067395568,0.004542162,2.363791383,1.199880906,1.439714188
33,K'naan - Waving Flag.mp3,happy,2,75.99954044,92219,1263.273973,0.333718314,0.310685262,0.096525332,0.526373964,0.288186018,0.083051181,0.256453838,0.132532118,0.017564762,7.665881763,58.61058842,3435.201075,0.657475056,36.21784053,1311.731972,0.01091064,2.760303387,7.61927479,4.516118526,2.565011024,6.57928133,2719.051263,832.9341011,693779.2168,2807.989342,417.317302,174153.7306,19.45091511,6.309187072,39.80584151,6133.112914,1616.907024,2614388.324,2.007062694,2.613242091,6.829034225,0.019607278,0.067454888,0.004550162,0.115158412,0.064214184,0.004123461,-6.42E-06,0.1091813,0.011920555,0.000117855,0.110110119,0.012124239,2.964027211,1.540749851,2.373910102
34,Katy Perry - Firework.mp3,happy,2,123.046875,149489,1245.741667,0.319271761,0.30490407,0.092966492,0.569835431,0.268900072,0.072307249,0.266542322,0.110853615,0.012288524,6.33452847,52.46348492,2752.41725,-1.78458196,29.99545366,899.7272405,0.011549971,2.631254261,6.923498987,4.338088989,2.197864294,4.830607414,2828.94605,806.4568169,650372.5975,2534.681964,346.703843,120203.5548,21.2542257,6.699803652,44.88736897,5649.520005,1382.805201,1912150.224,1.930650675,2.326983599,5.41485267,0.006470377,0.054561037,0.002976907,0.145506679,0.072876649,0.005311006,-3.64E-06,0.123941764,0.015361561,-2.05E-05,0.097580649,0.009521983,4.987646259,2.732255666,7.465221022
35,Katy Perry - I Kissed a Girl.mp3,happy,2,129.1992188,166941,1284.161538,0.424609873,0.296794544,0.088087002,0.490908643,0.282824218,0.079989538,0.252563492,0.139803491,0.019545016,1.153504436,8.226192745,67.67024708,0.07277011,44.70453179,1998.495162,-0.000554184,3.10420526,9.636090293,1.90388,0.702271402,0.493185133,2586.705161,959.9057074,921418.967,2606.904154,479.561531,229979.262,19.27737467,6.677743707,44.59226102,5426.754685,1876.585012,3521571.306,0.855481724,0.987174264,0.974513027,-0.000464537,0.07007857,0.004911006,0.109073792,0.063048166,0.003975071,-1.33E-06,0.053292528,0.002840093,2.33E-05,0.035375156,0.001251401,2.426485261,1.529056493,2.338013758
36,Kesha - Dancing With Tears In My Eyes.mp3,happy,2,117.4538352,153190,1287.310924,0.404095201,0.312037854,0.097367622,0.577615568,0.272759526,0.074397759,0.269704098,0.102922461,0.010593033,9.673783125,70.2800849,4939.290333,2.968818018,34.10427065,1163.101277,0.011664829,3.533767499,12.48751274,5.34232235,2.381651163,5.672262669,2586.532187,1009.867538,1019832.444,2561.445742,592.319355,350842.2183,19.65683227,7.477825911,55.91788036,5468.846097,1893.07787,3583743.822,2.417972435,2.938257429,8.633356722,0.007903469,0.054098024,0.002926596,0.11707091,0.083681721,0.00700263,-1.06E-05,0.127444252,0.016242037,-2.61E-05,0.13391684,0.017933721,5.310403628,2.329727607,5.427630722
37,Laura Jansen - Use Somebody.mp3,sad,1,129.1992188,152569,1250.565574,0.281130342,0.295547935,0.087348582,0.398251896,0.280725999,0.078807087,0.229530481,0.17506882,0.030649092,3.950106138,26.72243843,714.0887156,-7.668213253,65.72611657,4319.922399,0.008448898,2.532107626,6.41156903,3.215786219,1.818070412,3.305379868,1317.696272,934.5078528,873304.9269,1383.170278,807.8206468,652574.1975,24.80586969,5.856771171,34.30176855,2423.514512,1809.438438,3274067.462,1.181530762,1.59207335,2.534697551,-0.006783966,0.096021452,0.009220119,0.068756576,0.053719638,0.002885799,-1.93E-06,0.119287215,0.01422944,-4.08E-05,0.035870176,0.00128667,9.111510204,5.619955321,31.58389781
38,League of Legends - Burning Brighter.mp3,sad,1,151.9990809,196235,1344.075342,0.324089405,0.295542356,0.087345284,0.359859827,0.301359738,0.090817692,0.208220243,0.199944152,0.039977664,8.249232477,49.29405197,2429.903559,3.319539251,38.53048236,1484.598071,0.012989594,1.771574072,3.138474692,4.933689117,2.334563255,5.450185299,2212.502062,689.9593225,476043.8666,2425.560799,505.7492592,255782.3132,22.9208803,5.923006278,35.08200337,4675.367562,1626.425913,2645261.251,2.186471113,2.739766483,7.506320381,-0.020125905,0.103971676,0.010810109,0.095594849,0.038878603,0.001511546,-3.17E-05,0.167537481,0.028068809,-0.000231623,0.061699793,0.003806865,12.83831293,5.015746161,25.15770955
39,League of Legends - Get Jinxed.mp3,happy,2,89.10290948,109445,1257.988506,0.452530114,0.284025379,0.080670416,0.579252144,0.252445047,0.063728502,0.268786918,0.105294473,0.011086926,4.549776993,27.4669709,754.4344902,0.744114977,46.07069468,2122.508909,0.013679558,1.987664549,3.95081036,3.462542534,2.189370394,4.79334259,2727.59234,912.841228,833279.1075,2533.286186,362.2478199,131223.4831,19.03039829,7.429052799,55.19082549,5514.508321,1462.90677,2140096.217,1.966408862,2.61805056,6.854188736,0.002897829,0.05543358,0.003072882,0.136738402,0.079419146,0.006307401,1.29E-05,0.09932667,0.009865787,7.62E-05,0.068299763,0.004664857,2.449705215,1.419544668,2.015107065
40,Linkin Park - In the End.mp3,angry,4,103.359375,114020,1187.708333,0.392283811,0.290522472,0.084403307,0.512753815,0.2659971,0.070754457,0.257752831,0.129987736,0.016896812,3.010661633,17.40019048,302.7666289,-2.424366114,44.83900896,2010.536724,0.013677663,2.488881017,6.194528715,2.874552727,1.709442377,2.92219305,2232.005855,682.1114554,465276.0376,2143.177842,490.301717,240395.7737,20.14599599,7.283769336,53.05329574,4396.340083,1397.968996,1954317.313,1.602999383,2.125786632,4.518968803,-0.006133187,0.067674562,0.004579846,0.117040298,0.051461771,0.002648314,4.05E-07,0.078016989,0.00608665,5.24E-05,0.060906451,0.003709596,2.121142857,1.270160703,1.613308212
41,Linkin Park - Roads Untraveled.mp3,sad,1,99.38401442,124734,1272.795918,0.298325594,0.289053865,0.083552137,0.481603022,0.272188103,0.074086364,0.247894021,0.147925278,0.021881888,1.081165605,17.60949215,310.0942136,-8.400634075,47.73519719,2278.64905,0.000275448,3.317845269,11.00809723,1.753431559,1.183774471,1.401321888,3639.48644,1020.790592,1042013.433,2769.126829,313.9421788,98559.69166,22.84016437,8.611410023,74.15638258,7008.636786,1271.428472,1616530.359,0.700217137,1.002790473,1.005588732,0.011720811,0.08627112,0.007442706,0.291053607,0.134688175,0.018140904,-3.25E-05,0.058694497,0.003445044,-6.53E-05,0.035143379,0.001235057,1.926095238,1.015842498,1.03193598
42,Machinae Supremacy - Death from above.mp3,relax,3,99.38401442,122026,1271.104167,0.474093998,0.284134087,0.080732179,0.487018893,0.283986889,0.080648553,0.253747265,0.13764323,0.018945659,22.11286794,161.7201594,26153.40997,7.431293225,24.92652444,621.3316206,0.012522681,1.947079132,3.791117148,8.146737099,2.912524223,8.482796669,2258.78355,303.9983155,92414.97585,2481.360251,212.1562954,45010.29366,18.51735426,6.614681769,43.7540149,4966.319969,724.2450957,524530.9587,3.584976074,3.938301352,15.51021754,-0.006993686,0.071196245,0.005068905,0.077111779,0.030267295,0.000916109,-0.000689172,0.248119414,0.061563242,-0.005631799,0.114827216,0.013185289,1.989950113,1.031266651,1.063510906
43,Machinae Supremacy - Edge and Pearl.mp3,angry,4,123.046875,149078,1433.442308,0.422980605,0.306254246,0.093791663,0.500246482,0.279650431,0.078204364,0.246920246,0.149545061,0.022363725,13.47024773,100.5660779,10113.53602,5.567504793,56.59775283,3203.305626,0.013064685,1.542266813,2.378586922,5.861894131,3.677881002,13.52680874,1629.004255,990.4584443,981007.9299,1550.165576,860.8568074,741074.4428,21.41785613,7.300156228,53.29228096,3168.214176,1961.646888,3848058.512,2.781511369,4.105780732,16.85743542,-0.002884028,0.060380059,0.003645752,0.080054694,0.040728595,0.001658818,3.96E-07,0.206255674,0.042541403,-1.82E-05,0.074160367,0.00549976,2.92339229,1.844253577,3.401271257
44,Magic System - Magic in the Air.mp3,happy,2,129.1992188,162668,1280.850394,0.35159583,0.295835097,0.087518405,0.473733878,0.288565217,0.083269884,0.255017751,0.13527483,0.01829928,12.35239422,89.91046992,8083.892602,-1.617058777,34.84891623,1214.446963,0.013001072,2.419706467,5.854979387,5.881638527,3.426352739,11.73989296,3005.843303,595.1133473,354159.8962,2651.491055,294.72184,86860.96298,20.41536147,6.657935564,44.32810598,6064.892493,1087.293559,1182207.283,2.967544341,3.886427993,15.10432255,-0.006704283,0.079333349,0.00629378,0.147025377,0.064189879,0.004120341,1.14E-05,0.182228699,0.033207301,0.000204679,0.106646612,0.0113735,2.777106576,1.531538642,2.345610612
45,Miley Cyrus - Lets get Crazy.mp3,happy,2,123.046875,159253,1349.601695,0.405164263,0.303806267,0.092298248,0.496957256,0.279475626,0.078106626,0.255399621,0.125242311,0.015685636,4.930700428,34.03069389,1158.088126,1.4004277,45.2771528,2050.020566,0.013095444,2.877827481,8.28189101,3.726270437,1.870782256,3.499826193,2347.772477,900.1718801,810309.4137,2389.268734,674.0832192,454388.1864,19.64983974,8.874479652,78.75638909,5039.165362,1758.716899,3093085.13,1.677532321,2.156784205,4.651718109,0.007444439,0.065025353,0.004228297,0.096643218,0.06732779,0.004533031,-4.07E-06,0.111580588,0.012450228,-0.000156933,0.068321377,0.004667811,7.504689342,1.953757396,3.817167961
46,My Darkest Days - Can't Forget You.mp3,sad,1,161.4990234,192521,1617.823529,0.393048434,0.281240306,0.07909611,0.378933381,0.296526284,0.087927837,0.216558067,0.190881998,0.036435937,8.82812321,52.67126115,2774.261751,4.5621811,43.7035664,1910.001716,0.013436007,1.75952486,3.095927731,4.93493557,2.487243176,6.186378479,1894.498,836.456772,699659.9315,2170.375861,704.6087502,496473.4909,22.07275019,6.822237943,46.54293056,4162.312331,1937.239886,3752898.377,2.264424344,3.007900856,9.047467561,-0.021161244,0.105885988,0.011211842,0.078929984,0.045291714,0.002051339,8.39E-06,0.155775771,0.024266092,-2.58E-06,0.067858286,0.004604747,12.37855782,5.759039815,33.16653959
47,My Darkest Days - Without You.mp3,sad,1,53.83300781,60939,1218.78,0.302876619,0.294142774,0.086519971,0.449049509,0.279090156,0.077891315,0.237334008,0.164334725,0.027005902,3.76066382,23.33594711,544.5664277,-2.519093575,46.02045832,2117.882584,0.013154986,2.188790624,4.790804394,3.196904421,1.894014239,3.587290049,2346.960318,1002.21822,1004441.36,2242.792194,678.4665426,460316.8495,21.98228798,8.130868159,66.11101702,4789.025208,1946.327874,3788192.194,1.550427314,2.101311528,4.415510139,0.000417805,0.081786221,0.006688986,0.122651564,0.076188151,0.005804634,3.19E-05,0.108232871,0.011714354,-2.82E-05,0.048314329,0.002334274,9.582875283,4.32186361,18.67850506
48,One Direction - Best Song Ever.mp3,happy,2,117.4538352,139921,1238.238938,0.427366027,0.296932361,0.088168827,0.489105305,0.281639459,0.079320785,0.257372479,0.130739207,0.01709274,13.1010479,79.53107734,6325.192263,2.95615363,35.64644762,1270.669228,0.013053365,2.487371027,6.187014628,6.082351685,3.643235207,13.27316284,2809.648714,971.8242503,944442.3734,2562.195745,572.0641984,327257.4471,19.29714108,6.882053828,47.36266489,5764.298477,1928.074082,3717469.664,3.236965346,4.391036494,19.2812015,-0.011337214,0.073676882,0.005428283,0.146054483,0.075775601,0.005741942,3.14E-05,0.164838284,0.02717166,3.43E-05,0.133256808,0.017757379,1.604498866,0.729302514,0.531882157
49,One Direction - Live While you are Young.mp3,happy,2,123.046875,157294,1278.813008,0.434535951,0.287642669,0.082738305,0.550407009,0.26704318,0.07131206,0.269047488,0.104626874,0.010946783,10.18983695,67.46123224,4551.017855,2.658188308,38.95854824,1517.768481,0.012054859,3.455914524,11.9433452,5.442199707,3.003162384,9.018984795,2841.189465,749.283597,561425.9087,2616.465831,432.3480737,186924.8568,19.47702097,6.933777727,48.07727357,5930.297474,1428.857956,2041635.057,2.878150144,3.830235549,14.67070436,-0.006083079,0.053504672,0.00286275,0.140441895,0.065349607,0.004270571,-2.38E-06,0.143809974,0.02068131,-0.000156095,0.113732405,0.01293506,4.575492063,2.660985275,7.080842631
50,One Direction - What Makes You Beautiful.mp3,happy,2,123.046875,132280,1284.271845,0.43617543,0.294726051,0.086863445,0.555435686,0.267101533,0.071343229,0.269333931,0.10388728,0.010792567,12.87905681,92.24213506,8508.61148,2.882267828,36.94202628,1364.713306,0.012364478,3.531179086,12.46922574,5.916572094,3.71233058,13.78139782,2884.346751,911.0308955,829977.2926,2655.669441,477.4700006,227977.6014,19.35926151,7.421695705,55.08156713,6093.742319,1679.325354,2820133.646,3.038892807,4.159411832,17.30070679,0.00750701,0.059385017,0.00352658,0.143278612,0.075752293,0.00573841,4.64E-06,0.17067489,0.029129917,4.80E-05,0.120935574,0.014625413,7.668390023,1.669220427,2.786296834
51,One Republic - All The Right Moves.mp3,happy,2,95.703125,120349,1266.831579,0.39231577,0.294440807,0.086695389,0.475705297,0.276610028,0.076513107,0.250507889,0.143454282,0.020579131,13.29545282,70.47615548,4966.888491,3.161842897,36.69732736,1346.693835,0.01307085,2.669300447,7.125164877,6.134161472,2.832911253,8.025385857,2095.777474,767.3214571,588782.2185,2314.282848,626.8083481,392888.7052,19.82196678,6.745833876,45.50627468,4579.355643,1782.595004,3177644.95,3.044778947,3.804292437,14.47264095,0.014226101,0.067596102,0.004569233,0.091014,0.0440178,0.001937567,-1.80E-05,0.170286641,0.02899754,1.36E-05,0.11313235,0.012798929,7.718312925,2.248339181,5.055029072
52,Or4nges - CrowMachi.mp3,relax,3,117.4538352,146814,1299.238938,0.384424402,0.309917105,0.096048612,0.456862183,0.293145891,0.085934514,0.261160536,0.122998,0.015128508,5.13445291,53.2950818,2840.365744,-13.27203965,56.66379334,3210.785476,0.012098868,2.862852847,8.195926424,3.528975725,2.587898493,6.697218895,2360.662846,1002.569254,1005145.109,1906.859101,882.7454921,779239.6038,20.94585508,8.35763291,69.85002785,4402.640045,2373.214248,5632145.869,1.593333927,2.219539354,4.926354943,0.007833285,0.088295963,0.007796177,0.121449568,0.048624319,0.002364324,7.55E-06,0.107253499,0.011503313,2.61E-05,0.087400079,0.007638774,1.789097506,0.987120528,0.974406937
53,Paul Oakenfold - Jump.mp3,happy,2,123.046875,94420,993.8947368,0.472651775,0.256635785,0.065861926,0.387447739,0.267733142,0.071681036,0.234277307,0.168663798,0.028447477,28.52972174,125.341099,15710.39109,10.75737189,23.79599036,566.2491573,0.01368725,1.813475596,3.288693736,9.902039528,2.051474094,4.208546162,2617.439851,481.0017456,231362.6792,2747.401427,268.2809269,71974.65572,17.47676179,6.387220927,40.79659116,5902.160144,941.4170323,886266.0288,5.549079033,5.839680431,34.10186754,0.041546111,0.091430281,0.008359496,0.115567895,0.040717391,0.001657906,6.13E-05,0.244270161,0.059667911,0.001238371,0.151657701,0.02300006,2.680743764,1.118890445,1.251915828
54,Pokemon - Master Quest.mp3,happy,2,151.9990809,105406,949.6036036,0.446547186,0.280551514,0.078709152,0.531885741,0.258466339,0.066804848,0.265974687,0.11220873,0.012590799,7.032434533,27.38431742,749.9008404,2.401471867,30.20031419,912.0589773,-1.62E-18,2.035389232,4.142809325,5.762974262,2.089851618,4.367479324,3557.563607,759.2985749,576534.3259,2684.863035,351.9268832,123852.5311,19.07525808,6.599944577,43.55926842,6668.594563,1255.145344,1575389.836,3.254173101,3.675002069,13.50564021,0.006986982,0.05901747,0.003483062,0.238643133,0.075724306,0.005734171,-0.000132158,0.135042235,0.018236406,-0.00127452,0.119754747,0.014341199,2.953578231,1.495040542,2.235146221
55,Rise Against - Hero Of War.mp3,sad,1,151.9990809,196151,1282.03268,0.319922034,0.300488206,0.090293162,0.428341622,0.290743331,0.084531684,0.235509823,0.166938481,0.027868456,5.722351945,30.12685354,907.6273041,-3.197131131,46.59636978,2171.221677,0.008435165,3.45244784,11.91939609,4.187962055,1.685546875,2.841068268,1985.919642,993.2271416,986500.1548,2240.643917,647.6105107,419399.3735,24.98476635,6.237055311,38.90085896,4083.850306,2101.258001,4415285.185,1.684955486,2.105500214,4.43313115,0.021398733,0.098066749,0.009617087,0.086985302,0.062888118,0.003954915,1.11E-05,0.133877143,0.017923091,-2.28E-05,0.061105367,0.003733866,4.035628118,2.288015202,5.235013564
56,Shakaponk - I'm Picky.mp3,angry,4,117.4538352,110503,1139.206186,0.350081544,0.304619075,0.092792781,0.560780191,0.265961147,0.070735332,0.270397407,0.10108697,0.010218575,1.239403164,6.940108685,48.16510857,-5.868864023,56.54183653,3196.979278,0.013690035,3.986155773,15.88943785,1.826951385,0.991128683,0.982336104,1988.323018,769.5285005,592174.113,2162.269107,548.7230091,301096.9407,20.34643813,6.968547998,48.5606612,4113.304294,1798.798489,3235676.005,0.878607108,1.174057204,1.378410319,0.001158114,0.055254166,0.003053023,0.092890028,0.052983432,0.002807244,-5.46E-06,0.048540246,0.002356155,-2.60E-05,0.042020932,0.001765759,5.358004535,1.932646289,3.735121678
57,Simple Plan - How Could This Happen To Me.mp3,sad,1,92.28515625,117306,1275.065217,0.270525811,0.289634059,0.083887888,0.431927315,0.269526935,0.072644768,0.237570507,0.163992645,0.026893588,4.118783334,24.93919892,621.9636426,-3.846752485,46.89472303,2199.115048,0.009680363,2.050427687,4.204253701,3.549631596,1.380890965,1.906859994,1617.68338,550.5014982,303051.8995,1789.72623,429.2865292,184286.9242,22.98434091,7.325086572,53.65689329,3097.885434,1175.718086,1382313.017,1.524289773,1.746125469,3.048954153,-0.024032963,0.083720423,0.007009109,0.076536007,0.035777353,0.001280019,-3.82E-06,0.120497949,0.014519756,-3.08E-05,0.038667526,0.001495178,5.782929705,3.386759737,11.47014151
58,Spooky Scary Skeleton (Remix).mp3,relax,3,129.1992188,156694,1273.934959,0.508662312,0.29679388,0.088086607,0.527496171,0.294912518,0.086973393,0.26294544,0.119134499,0.014193029,25.66208824,232.5785152,54092.76576,4.115012539,29.20754027,853.0804085,0.008671213,3.789491461,14.36024553,8.592530251,3.959408045,15.67691231,2898.494336,1186.81112,1408520.634,2560.452804,548.6389457,301004.6927,19.36479835,7.264832301,52.77778836,5831.098073,1850.582976,3424657.351,3.231671621,3.834197131,14.70106764,0.003740571,0.060717876,0.00368666,0.1438695,0.119667531,0.014320318,2.98E-05,0.215622365,0.046493005,0.000414458,0.195752844,0.038319174,1.912163265,0.991148735,0.982375815
59,Stemm - Face The Pain.mp3,angry,4,112.3471467,125280,1204.615385,0.515269814,0.279728012,0.078247761,0.570368648,0.251558343,0.0632816,0.270310897,0.101318075,0.010265352,17.56883863,82.31114466,6775.124535,6.275447601,33.20979592,1102.890545,0.009572779,1.947634479,3.793280064,7.52352047,3.128091812,9.784957886,2605.17918,703.0291156,494249.9374,2456.471351,551.4083485,304051.1668,18.87956994,6.737332035,45.39164296,5267.034818,1562.876519,2442583.013,4.367678532,5.196493196,27.00354153,0.016430823,0.054401356,0.002959508,0.129248841,0.048445472,0.002346964,-9.90E-06,0.200142711,0.040057104,-0.000424632,0.133991182,0.017953636,1.407129252,0.771985797,0.595962071
60,System of a Down - Lonely Day.mp3,sad,1,75.99954044,95634,1275.12,0.34864247,0.289630099,0.083885594,0.433863445,0.277158072,0.076816597,0.240750149,0.1592881,0.025372699,7.462660592,40.34433027,1627.664985,-2.357211371,45.30043597,2052.129499,0.011752454,2.373378112,5.632923664,4.405441284,2.891358852,8.359955788,1983.846749,836.6255306,699942.2785,2010.810819,612.2485286,374848.2608,24.62025512,6.364113652,40.50194257,4063.196264,1642.414746,2697526.197,2.109520325,3.199344696,10.23580648,0.011056715,0.089448063,0.008000956,0.107551315,0.049845251,0.002484549,-4.05E-06,0.152906761,0.023380477,1.70E-05,0.068197735,0.004650931,2.655201814,1.583769004,2.508324259
61,Taylor Swift - Love Story.mp3,sad,1,117.4538352,152141,1289.330508,0.301311051,0.298763187,0.089259442,0.43087856,0.295626454,0.087395001,0.234013528,0.16902959,0.028571002,3.674599711,29.10227793,846.9425808,-5.143686607,55.68608552,3100.94012,0.01389207,2.668250004,7.119558084,2.93693614,2.00253582,4.010149479,1881.766381,962.0005123,925444.9856,2188.217655,730.3799954,533454.9377,22.69258187,5.617044415,31.55118796,3941.93033,1879.85833,3533867.34,1.058953722,1.51801574,2.304371786,-0.006170883,0.089460652,0.008003208,0.075154118,0.061471438,0.003778738,4.66E-05,0.10853871,0.011780651,-0.000195898,0.041570403,0.001728098,2.838639456,1.454427137,2.115358298
62,Tekken 5 - Poolside.mp3,relax,3,95.703125,118697,1276.311828,0.500476054,0.280250627,0.078540414,0.547013471,0.26184079,0.068560599,0.260155574,0.125109594,0.01565241,1.740055392,11.74896005,138.0380623,2.905994912,35.33066561,1248.255933,0.013513441,1.621615401,2.629636507,2.364928722,0.60954833,0.371549159,2884.18944,421.0599906,177291.5157,2940.945437,219.9756549,48389.28874,18.83286341,6.684104407,44.67725172,6598.022603,837.8401527,701976.1214,1.00771308,1.056546576,1.116290667,0.000322874,0.047801087,0.002284944,0.094821989,0.03369975,0.001135673,-7.43E-06,0.069413662,0.004818256,-0.00016171,0.030544639,0.000932975,4.368834467,2.059942085,4.243361393
63,The Fat Rat - Unity.mp3,relax,3,103.359375,132082,1282.349515,0.361239654,0.296379258,0.087840665,0.412078513,0.293714112,0.08626798,0.235266601,0.16728108,0.02798296,13.09603456,154.067537,23736.80596,1.657267574,28.29517191,800.6167532,0.006176826,3.217252972,10.35071668,5.825383186,3.67489624,13.50486279,2954.191448,787.1814175,619654.584,2753.858068,219.9833827,48392.68866,21.5562148,7.366462989,54.26477697,6257.024665,1054.981532,1112986.034,2.427223046,3.103138836,9.629470639,0.006947026,0.083542123,0.006979286,0.145542771,0.082924671,0.006876501,2.50E-05,0.19787246,0.039153509,0.000162774,0.101857424,0.010374935,1.496526077,0.891823867,0.795349811
64,Three Days Grace - Animal I have Become.mp3,angry,4,123.046875,130253,1173.45045,0.457370252,0.277751404,0.077145842,0.447804324,0.287201964,0.082484968,0.248450557,0.146988619,0.021605654,5.167413858,26.76395342,716.3092029,5.521107628,39.2905115,1543.744294,0.012995916,3.082600362,9.502424993,4.124792099,1.211073518,1.466699004,2286.024572,950.5382291,903522.925,2305.758833,636.2765269,404847.8186,19.586825,7.570719957,57.31580067,4844.529039,1904.090402,3625560.257,2.032851638,2.304631267,5.311325278,0.018771639,0.075586262,0.005713283,0.10460292,0.060832177,0.003700554,1.98E-07,0.109219007,0.011928792,-0.000369615,0.067188658,0.004514316,2.491501134,1.419537072,2.015085498
65,Three Days Grace - Gone Forever.mp3,sad,1,151.9990809,191720,1261.315789,0.341778424,0.296622791,0.08798508,0.453216405,0.291850927,0.085176964,0.240813725,0.15919197,0.025342083,12.91739429,70.51776555,4972.755259,2.13751191,31.64462943,1001.382572,0.013240036,2.473299237,6.117209113,6.296798229,2.67741251,7.16853714,2650.423046,883.0137856,779713.3455,2628.304155,459.3094783,210965.1969,21.29020142,7.122901447,50.73572503,5667.469896,1641.73021,2695278.082,3.02198835,3.80929194,14.51070508,-0.002487458,0.084260697,0.007099865,0.120346967,0.067657955,0.004577599,1.19E-05,0.182948813,0.033470269,7.75E-05,0.109429114,0.011974731,3.984544218,2.346334768,5.505286844
66,Three Days Grace - I Hate Everything About You.mp3,angry,4,89.10290948,113152,1285.818182,0.359532167,0.298315373,0.088992062,0.480430188,0.275714595,0.076018538,0.252341646,0.13904866,0.01933453,9.583000603,56.74306174,3219.775056,-0.347878265,39.12436742,1530.716126,0.013000477,2.520549315,6.353168851,4.965517998,2.885135889,8.324008942,2155.031704,714.0394992,509852.4065,2195.807127,541.8686834,293621.67,20.91573395,6.76867607,45.81497575,4357.615317,1408.632453,1984245.389,2.348113724,3.036205477,9.218543699,-0.019382843,0.071776631,0.005151885,0.098622231,0.048965794,0.002397649,3.55E-06,0.158572525,0.025145246,5.08E-05,0.078693889,0.006192728,4.071619048,1.648065214,2.716118949
67,Three Days Grace - Let it Die.mp3,angry,4,99.38401442,123670,1274.948454,0.389042501,0.287041651,0.08239291,0.448754688,0.285501512,0.081511113,0.241731086,0.157795485,0.024899415,16.92050826,81.816453,6693.931982,5.165342101,24.38895281,594.821019,0.011858547,1.898832728,3.60556573,7.461583138,2.389023781,5.707434654,2828.938283,753.1519836,567237.9103,2754.292906,361.9811066,131030.3215,20.20208177,6.833221344,46.69291394,6087.254858,1376.481268,1894700.68,3.765684715,4.223021709,17.83391235,0.008504836,0.081558868,0.006651849,0.135055873,0.066611145,0.004437045,-4.08E-06,0.206723243,0.0427345,-4.98E-05,0.119062327,0.014175837,4.373478458,3.221531479,10.37826507
68,Three Days Grace - Over and Over.mp3,sad,1,135.9991776,156990,1207.615385,0.358138283,0.284385959,0.080875374,0.403314012,0.291271396,0.084839026,0.225661001,0.180029014,0.032410446,14.31468801,64.29257085,4133.534666,5.791951103,33.7440498,1138.660897,0.013211075,1.795472766,3.223722454,6.770902157,2.408128977,5.79908514,2325.336067,829.6420852,688305.9896,2408.909097,713.6214029,509255.5067,21.37460772,6.682280728,44.65287572,5108.806608,1850.993839,3426178.191,3.380720917,3.890848515,15.13870217,-0.014691733,0.094997521,0.009024529,0.110540904,0.046147704,0.002129611,2.17E-06,0.201881245,0.040756039,-4.11E-05,0.098803937,0.009762218,2.245369615,1.244123783,1.547843988
69,Three Days Grace - Riot.mp3,angry,4,129.1992188,167751,1290.392308,0.453365449,0.285361155,0.081430989,0.470183233,0.280472738,0.078664957,0.250879255,0.142803826,0.020392933,16.82181334,78.72920585,6198.287854,7.917290778,24.23522618,587.3461882,0.013252582,1.949989179,3.802457798,7.561780453,2.166385174,4.693224907,2845.595211,653.2217581,426698.6652,2703.709743,394.0139635,155247.0034,18.09022595,7.335753931,53.81328574,6078.946575,1254.802078,1574528.254,4.250819644,4.602592534,21.18385803,0.024909688,0.077593465,0.006020746,0.144742889,0.054636414,0.002985138,2.36E-06,0.179258555,0.032133631,-0.000258519,0.139427707,0.019440087,1.991111111,0.994938478,0.989902576
70,Three Grace - Home.mp3,sad,1,83.35433468,103925,1267.378049,0.469550774,0.277796901,0.077171118,0.441880217,0.289791682,0.083979219,0.242389796,0.15678176,0.02458052,7.639213657,50.69834188,2570.321869,6.192285993,25.86490989,668.9935638,0.012713479,2.149671137,4.621085996,4.999515533,1.386308789,1.921852112,2614.584772,645.9061323,417194.7317,2561.569515,346.6198824,120145.3429,20.40218611,7.682333883,59.01825389,5497.566757,1169.016803,1366600.286,2.313136678,2.495507707,6.227558718,0.018352358,0.083924054,0.007043247,0.107556606,0.048689647,0.002370682,-2.57E-06,0.154829338,0.023972124,-2.63E-06,0.060455248,0.003654837,3.795301587,2.15988612,4.665108052
71,VAST - Touched ( Cover ).mp3,sad,1,151.9990809,137586,1083.354331,0.311471745,0.30520832,0.093152119,0.351681663,0.295965828,0.087595772,0.219265055,0.187766261,0.035256169,0.171654417,1.323896213,1.752701184,-11.50778513,75.32365406,5673.652861,0.008198219,3.042563246,9.257191107,0.709378898,0.302621216,0.091579594,2344.698909,1094.847828,1198691.767,2612.313114,650.9404993,423723.5336,21.97035588,5.530943425,30.59133517,5154.243833,2401.86177,5768939.962,0.238910832,0.303094723,0.091866411,-0.013508287,0.110898603,0.0122985,0.074836282,0.066042419,0.004361601,-1.05E-06,0.025879189,0.000669732,1.13E-06,0.006502948,4.23E-05,1.832054422,1.002470281,1.004946664
72,Voicians - Loner.mp3,sad,1,172.265625,220316,1280.906977,0.500812199,0.266357649,0.070946397,0.542837788,0.261228329,0.06824024,0.270599884,0.100543703,0.010109036,18.83754964,89.10089368,7938.969255,6.179920571,25.08204243,629.1088526,0.01321851,1.631252247,2.660983895,7.996220589,2.41861105,5.849678993,2718.559883,483.7485004,234012.6116,2510.233383,403.0306884,162433.7358,18.47998372,6.889697972,47.46793814,5591.070358,1114.861498,1242916.161,4.612835166,4.944872405,24.4517631,-0.002304568,0.056423337,0.003183593,0.134021865,0.040028773,0.001602303,0.000263716,0.21729368,0.047216546,0.001940815,0.11817994,0.013966499,2.652879819,1.126104961,1.268112384
73,Waysons - Eternal Minds.mp3,relax,3,129.1992188,161819,1274.165354,0.351867542,0.289378758,0.083740065,0.313733958,0.311752968,0.097189913,0.190627286,0.216782313,0.046994571,14.234461,77.17092086,5955.351026,4.309867391,31.62715751,1000.277092,0.00662311,1.537609042,2.364241565,6.816382885,2.192842722,4.808559418,2296.500372,787.8740469,620745.5138,2405.417641,546.2383025,298376.2831,21.22877315,6.584894818,43.36083976,4821.425012,1826.28827,3335328.845,3.092973793,3.516592525,12.36642298,-0.028111703,0.124325209,0.015456758,0.098219739,0.047591027,0.002264906,3.14E-08,0.228647158,0.052279521,-0.000112067,0.066621438,0.004438416,3.43539229,2.246168784,5.045274205
74,You Are Not.mp3,angry,4,135.9991776,173553,1345.372093,0.355462316,0.285731936,0.081642739,0.53462494,0.251478868,0.063241621,0.262925405,0.119178709,0.014203565,10.42259281,54.27696853,2945.989312,2.928118091,45.29371491,2051.52061,0.012247643,1.526604782,2.33052216,5.492152214,2.608858824,6.806144714,1745.214664,700.0754685,490105.6615,1857.713276,521.2324515,271683.2685,21.10040274,7.120922491,50.70753713,3556.778493,1400.086823,1960243.111,2.94442841,3.708095627,13.74997318,-0.005259107,0.065379961,0.004274539,0.087514777,0.044615966,0.001990584,5.49E-06,0.163635641,0.026776621,-3.69E-07,0.081931576,0.006712784,4.022857143,2.044145797,4.178532038
75,You Blocked Me On Facebook Now You're Going To Die.mp3,angry,4,129.1992188,154584,1246.645161,0.509162746,0.289091204,0.083573724,0.537313038,0.286280175,0.081956339,0.258745945,0.127999489,0.016383869,25.33818911,192.0075562,36866.90163,7.384388596,22.34578119,499.3339372,0.007296479,3.023936107,9.144189578,9.175350189,3.303755045,10.91479683,3228.688312,718.0598856,515609.9994,2793.571303,278.4994332,77561.93431,20.94360279,14.17505629,200.9322208,6588.955991,1108.622534,1229043.924,4.414053032,4.947089888,24.47369836,-0.014738246,0.066149936,0.004375814,0.131870518,0.077565147,0.006016352,7.32E-05,0.24151288,0.058328472,0.000708924,0.194600791,0.037869468,4.066975057,2.237387826,5.005904283
================================================
FILE: Feature-Extraction.py
================================================
import librosa
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
'''
function: extract_features
input: path to mp3 files
output: csv file containing features extracted
This function reads the content in a directory and for each mp3 file detected
reads the file and extracts relevant features using librosa library for audio
signal processing
'''
def extract_feature(path):
id = 1 # Song ID
feature_set = pd.DataFrame() # Feature Matrix
# Individual Feature Vectors
songname_vector = pd.Series()
tempo_vector = pd.Series()
total_beats = pd.Series()
average_beats = pd.Series()
chroma_stft_mean = pd.Series()
chroma_stft_std = pd.Series()
chroma_stft_var = pd.Series()
chroma_cq_mean = pd.Series()
chroma_cq_std = pd.Series()
chroma_cq_var = pd.Series()
chroma_cens_mean = pd.Series()
chroma_cens_std = pd.Series()
chroma_cens_var = pd.Series()
mel_mean = pd.Series()
mel_std = pd.Series()
mel_var = pd.Series()
mfcc_mean = pd.Series()
mfcc_std = pd.Series()
mfcc_var = pd.Series()
mfcc_delta_mean = pd.Series()
mfcc_delta_std = pd.Series()
mfcc_delta_var = pd.Series()
rmse_mean = pd.Series()
rmse_std = pd.Series()
rmse_var = pd.Series()
cent_mean = pd.Series()
cent_std = pd.Series()
cent_var = pd.Series()
spec_bw_mean = pd.Series()
spec_bw_std = pd.Series()
spec_bw_var = pd.Series()
contrast_mean = pd.Series()
contrast_std = pd.Series()
contrast_var = pd.Series()
rolloff_mean = pd.Series()
rolloff_std = pd.Series()
rolloff_var = pd.Series()
poly_mean = pd.Series()
poly_std = pd.Series()
poly_var = pd.Series()
tonnetz_mean = pd.Series()
tonnetz_std = pd.Series()
tonnetz_var = pd.Series()
zcr_mean = pd.Series()
zcr_std = pd.Series()
zcr_var = pd.Series()
harm_mean = pd.Series()
harm_std = pd.Series()
harm_var = pd.Series()
perc_mean = pd.Series()
perc_std = pd.Series()
perc_var = pd.Series()
frame_mean = pd.Series()
frame_std = pd.Series()
frame_var = pd.Series()
# Traversing over each file in path
file_data = [f for f in listdir(path) if isfile (join(path, f))]
for line in file_data:
if ( line[-1:] == '\n' ):
line = line[:-1]
# Reading Song
songname = path + line
y, sr = librosa.load(songname, duration=60)
S = np.abs(librosa.stft(y))
# Extracting Features
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr)
rmse = librosa.feature.rmse(y=y)
cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
poly_features = librosa.feature.poly_features(S=S, sr=sr)
tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
harmonic = librosa.effects.harmonic(y)
percussive = librosa.effects.percussive(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
mfcc_delta = librosa.feature.delta(mfcc)
onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
frames_to_time = librosa.frames_to_time(onset_frames[:20], sr=sr)
# Transforming Features
songname_vector.set_value(id, line) # song name
tempo_vector.set_value(id, tempo) # tempo
total_beats.set_value(id, sum(beats)) # beats
average_beats.set_value(id, np.average(beats))
chroma_stft_mean.set_value(id, np.mean(chroma_stft)) # chroma stft
chroma_stft_std.set_value(id, np.std(chroma_stft))
chroma_stft_var.set_value(id, np.var(chroma_stft))
chroma_cq_mean.set_value(id, np.mean(chroma_cq)) # chroma cq
chroma_cq_std.set_value(id, np.std(chroma_cq))
chroma_cq_var.set_value(id, np.var(chroma_cq))
chroma_cens_mean.set_value(id, np.mean(chroma_cens)) # chroma cens
chroma_cens_std.set_value(id, np.std(chroma_cens))
chroma_cens_var.set_value(id, np.var(chroma_cens))
mel_mean.set_value(id, np.mean(melspectrogram)) # melspectrogram
mel_std.set_value(id, np.std(melspectrogram))
mel_var.set_value(id, np.var(melspectrogram))
mfcc_mean.set_value(id, np.mean(mfcc)) # mfcc
mfcc_std.set_value(id, np.std(mfcc))
mfcc_var.set_value(id, np.var(mfcc))
mfcc_delta_mean.set_value(id, np.mean(mfcc_delta)) # mfcc delta
mfcc_delta_std.set_value(id, np.std(mfcc_delta))
mfcc_delta_var.set_value(id, np.var(mfcc_delta))
rmse_mean.set_value(id, np.mean(rmse)) # rmse
rmse_std.set_value(id, np.std(rmse))
rmse_var.set_value(id, np.var(rmse))
cent_mean.set_value(id, np.mean(cent)) # cent
cent_std.set_value(id, np.std(cent))
cent_var.set_value(id, np.var(cent))
spec_bw_mean.set_value(id, np.mean(spec_bw)) # spectral bandwidth
spec_bw_std.set_value(id, np.std(spec_bw))
spec_bw_var.set_value(id, np.var(spec_bw))
contrast_mean.set_value(id, np.mean(contrast)) # contrast
contrast_std.set_value(id, np.std(contrast))
contrast_var.set_value(id, np.var(contrast))
rolloff_mean.set_value(id, np.mean(rolloff)) # rolloff
rolloff_std.set_value(id, np.std(rolloff))
rolloff_var.set_value(id, np.var(rolloff))
poly_mean.set_value(id, np.mean(poly_features)) # poly features
poly_std.set_value(id, np.std(poly_features))
poly_var.set_value(id, np.var(poly_features))
tonnetz_mean.set_value(id, np.mean(tonnetz)) # tonnetz
tonnetz_std.set_value(id, np.std(tonnetz))
tonnetz_var.set_value(id, np.var(tonnetz))
zcr_mean.set_value(id, np.mean(zcr)) # zero crossing rate
zcr_std.set_value(id, np.std(zcr))
zcr_var.set_value(id, np.var(zcr))
harm_mean.set_value(id, np.mean(harmonic)) # harmonic
harm_std.set_value(id, np.std(harmonic))
harm_var.set_value(id, np.var(harmonic))
perc_mean.set_value(id, np.mean(percussive)) # percussive
perc_std.set_value(id, np.std(percussive))
perc_var.set_value(id, np.var(percussive))
frame_mean.set_value(id, np.mean(frames_to_time)) # frames
frame_std.set_value(id, np.std(frames_to_time))
frame_var.set_value(id, np.var(frames_to_time))
print(songname)
id = id+1
# Concatenating Features into one csv and json format
feature_set['song_name'] = songname_vector # song name
feature_set['tempo'] = tempo_vector # tempo
feature_set['total_beats'] = total_beats # beats
feature_set['average_beats'] = average_beats
feature_set['chroma_stft_mean'] = chroma_stft_mean # chroma stft
feature_set['chroma_stft_std'] = chroma_stft_std
feature_set['chroma_stft_var'] = chroma_stft_var
feature_set['chroma_cq_mean'] = chroma_cq_mean # chroma cq
feature_set['chroma_cq_std'] = chroma_cq_std
feature_set['chroma_cq_var'] = chroma_cq_var
feature_set['chroma_cens_mean'] = chroma_cens_mean # chroma cens
feature_set['chroma_cens_std'] = chroma_cens_std
feature_set['chroma_cens_var'] = chroma_cens_var
feature_set['melspectrogram_mean'] = mel_mean # melspectrogram
feature_set['melspectrogram_std'] = mel_std
feature_set['melspectrogram_var'] = mel_var
feature_set['mfcc_mean'] = mfcc_mean # mfcc
feature_set['mfcc_std'] = mfcc_std
feature_set['mfcc_var'] = mfcc_var
feature_set['mfcc_delta_mean'] = mfcc_delta_mean # mfcc delta
feature_set['mfcc_delta_std'] = mfcc_delta_std
feature_set['mfcc_delta_var'] = mfcc_delta_var
feature_set['rmse_mean'] = rmse_mean # rmse
feature_set['rmse_std'] = rmse_std
feature_set['rmse_var'] = rmse_var
feature_set['cent_mean'] = cent_mean # cent
feature_set['cent_std'] = cent_std
feature_set['cent_var'] = cent_var
feature_set['spec_bw_mean'] = spec_bw_mean # spectral bandwidth
feature_set['spec_bw_std'] = spec_bw_std
feature_set['spec_bw_var'] = spec_bw_var
feature_set['contrast_mean'] = contrast_mean # contrast
feature_set['contrast_std'] = contrast_std
feature_set['contrast_var'] = contrast_var
feature_set['rolloff_mean'] = rolloff_mean # rolloff
feature_set['rolloff_std'] = rolloff_std
feature_set['rolloff_var'] = rolloff_var
feature_set['poly_mean'] = poly_mean # poly features
feature_set['poly_std'] = poly_std
feature_set['poly_var'] = poly_var
feature_set['tonnetz_mean'] = tonnetz_mean # tonnetz
feature_set['tonnetz_std'] = tonnetz_std
feature_set['tonnetz_var'] = tonnetz_var
feature_set['zcr_mean'] = zcr_mean # zero crossing rate
feature_set['zcr_std'] = zcr_std
feature_set['zcr_var'] = zcr_var
feature_set['harm_mean'] = harm_mean # harmonic
feature_set['harm_std'] = harm_std
feature_set['harm_var'] = harm_var
feature_set['perc_mean'] = perc_mean # percussive
feature_set['perc_std'] = perc_std
feature_set['perc_var'] = perc_var
feature_set['frame_mean'] = frame_mean # frames
feature_set['frame_std'] = frame_std
feature_set['frame_var'] = frame_var
# Converting Dataframe into CSV Excel and JSON file
feature_set.to_csv('Emotion_features.csv')
feature_set.to_json('Emotion_features.json')
# Extracting Feature Function Call
extract_feature('Dataset/')
================================================
FILE: README.md
================================================
# Music Emotion Recognition
A video explanation of the repo and research problem domain: https://youtu.be/5b5unjdikPo
### - Introduction
The study of music and emotion seeks to understand the psychological relationship between human affect and music. It is a branch of music psychology with numerous areas of study, including the nature of emotional reactions to music, how characteristics of the listener may determine which emotions are felt, and which components of a musical composition or performance may elicit certain reactions. [1]
### - Approach
There Exists 3 Approaches to solve this problem:
- Thayers Emotional Model
- Dataset Annotation by Music and Emotion Psychologist Experts
- Classification based on Tags Generated by the Song
Results can be improved by weighting
- Music Lyrics
- Music Genre
### - Visualizations
- Plukchik Wheel of Emotion
- Basic Emotion Wheel
- 2D Grid Representation of Emotion Labels
### - Reference
[1] Wikipedia: Music and Emotion - https://en.wikipedia.org/wiki/Music_and_emotion
[2] Wikipedia: Emotion - https://en.wikipedia.org/wiki/Emotion
================================================
FILE: SourceCode/Emotion-Recognition-RandomSeed.py
================================================
"""
@author: Danyal
The following code classifies piece of music as one of
the four emotions mentioned in the document
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
from pandas.tools.plotting import scatter_matrix
s
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
labels = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
plt.style.use('ggplot')
array = np.array(data)
result = []
xlabel = []
color = []
colors = ['red', 'green', 'blue']
index = 0
for random_seed in range(1, 11):
features = array[:, 5:]
labels = data.ix[:, 'class'].dropna()
test_size = 0.30
train_d, test_d, train_l, test_l = train_test_split(features, labels, test_size=test_size, random_state=random_seed)
for neighbors in range(1, 10):
kNN = KNeighborsClassifier(n_neighbors=neighbors)
kNN.fit(train_d, train_l)
prediction = kNN.predict(test_d)
xlabel.append(neighbors)
result.append(accuracy_score(prediction, test_l))
color.append(colors[index])
index = (index+1)%3
plt.figure(figsize=(10, 10))
plt.xlabel('kNN Neighbors for k=1,2...10')
plt.ylabel('Accuracy Score')
plt.title('kNN Classifier Results')
plt.ylim(0, 1)
plt.scatter(xlabel, result, color=color)
plt.savefig('10-folds kNN Result.png')
plt.show()
================================================
FILE: SourceCode/Emotion-Recognition.py
================================================
"""
@author: Danyal
The following code classifies piece of music as one of
the four emotions mentioned in the document
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
featureName = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
for name in featureName:
feature[name] = (feature[name]-feature[name].min())/(feature[name].max()-feature[name].min())
plt.style.use('ggplot')
array = np.array(data)
features = feature.values
labels = data.ix[:, 'class'].dropna()
test_size = 0.20
random_seed = 7
train_d, test_d, train_l, test_l = train_test_split(features, labels, test_size=test_size, random_state=random_seed)
result = []
xlabel = [i for i in range(1, 11)]
for neighbors in range(1, 11):
kNN = KNeighborsClassifier(n_neighbors=neighbors)
kNN.fit(train_d, train_l)
prediction = kNN.predict(test_d)
result.append(accuracy_score(prediction, test_l)*100)
plt.figure(figsize=(10, 10))
plt.xlabel('kNN Neighbors for k=1,2...20')
plt.ylabel('Accuracy Score')
plt.title('kNN Classifier Results')
plt.ylim(0, 100)
plt.xlim(0, xlabel[len(xlabel)-1]+1)
plt.plot(xlabel, result)
plt.savefig('1-fold 10NN Result.png')
plt.show()
================================================
FILE: SourceCode/Feature-Extraction.py
================================================
import librosa
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
'''
function: extract_features
input: path to mp3 files
output: csv file containing features extracted
This function reads the content in a directory and for each mp3 file detected
reads the file and extracts relevant features using librosa library for audio
signal processing
'''
def extract_feature(path):
id = 1 # Song ID
feature_set = pd.DataFrame() # Feature Matrix
# Individual Feature Vectors
songname_vector = pd.Series()
tempo_vector = pd.Series()
total_beats = pd.Series()
average_beats = pd.Series()
chroma_stft_mean = pd.Series()
chroma_stft_std = pd.Series()
chroma_stft_var = pd.Series()
chroma_cq_mean = pd.Series()
chroma_cq_std = pd.Series()
chroma_cq_var = pd.Series()
chroma_cens_mean = pd.Series()
chroma_cens_std = pd.Series()
chroma_cens_var = pd.Series()
mel_mean = pd.Series()
mel_std = pd.Series()
mel_var = pd.Series()
mfcc_mean = pd.Series()
mfcc_std = pd.Series()
mfcc_var = pd.Series()
mfcc_delta_mean = pd.Series()
mfcc_delta_std = pd.Series()
mfcc_delta_var = pd.Series()
rmse_mean = pd.Series()
rmse_std = pd.Series()
rmse_var = pd.Series()
cent_mean = pd.Series()
cent_std = pd.Series()
cent_var = pd.Series()
spec_bw_mean = pd.Series()
spec_bw_std = pd.Series()
spec_bw_var = pd.Series()
contrast_mean = pd.Series()
contrast_std = pd.Series()
contrast_var = pd.Series()
rolloff_mean = pd.Series()
rolloff_std = pd.Series()
rolloff_var = pd.Series()
poly_mean = pd.Series()
poly_std = pd.Series()
poly_var = pd.Series()
tonnetz_mean = pd.Series()
tonnetz_std = pd.Series()
tonnetz_var = pd.Series()
zcr_mean = pd.Series()
zcr_std = pd.Series()
zcr_var = pd.Series()
harm_mean = pd.Series()
harm_std = pd.Series()
harm_var = pd.Series()
perc_mean = pd.Series()
perc_std = pd.Series()
perc_var = pd.Series()
frame_mean = pd.Series()
frame_std = pd.Series()
frame_var = pd.Series()
# Traversing over each file in path
file_data = [f for f in listdir(path) if isfile (join(path, f))]
for line in file_data:
if ( line[-1:] == '\n' ):
line = line[:-1]
# Reading Song
songname = path + line
y, sr = librosa.load(songname, duration=60)
S = np.abs(librosa.stft(y))
# Extracting Features
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr)
rmse = librosa.feature.rmse(y=y)
cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
poly_features = librosa.feature.poly_features(S=S, sr=sr)
tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
harmonic = librosa.effects.harmonic(y)
percussive = librosa.effects.percussive(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
mfcc_delta = librosa.feature.delta(mfcc)
onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
frames_to_time = librosa.frames_to_time(onset_frames[:20], sr=sr)
# Transforming Features
songname_vector.set_value(id, line) # song name
tempo_vector.set_value(id, tempo) # tempo
total_beats.set_value(id, sum(beats)) # beats
average_beats.set_value(id, np.average(beats))
chroma_stft_mean.set_value(id, np.mean(chroma_stft)) # chroma stft
chroma_stft_std.set_value(id, np.std(chroma_stft))
chroma_stft_var.set_value(id, np.var(chroma_stft))
chroma_cq_mean.set_value(id, np.mean(chroma_cq)) # chroma cq
chroma_cq_std.set_value(id, np.std(chroma_cq))
chroma_cq_var.set_value(id, np.var(chroma_cq))
chroma_cens_mean.set_value(id, np.mean(chroma_cens)) # chroma cens
chroma_cens_std.set_value(id, np.std(chroma_cens))
chroma_cens_var.set_value(id, np.var(chroma_cens))
mel_mean.set_value(id, np.mean(melspectrogram)) # melspectrogram
mel_std.set_value(id, np.std(melspectrogram))
mel_var.set_value(id, np.var(melspectrogram))
mfcc_mean.set_value(id, np.mean(mfcc)) # mfcc
mfcc_std.set_value(id, np.std(mfcc))
mfcc_var.set_value(id, np.var(mfcc))
mfcc_delta_mean.set_value(id, np.mean(mfcc_delta)) # mfcc delta
mfcc_delta_std.set_value(id, np.std(mfcc_delta))
mfcc_delta_var.set_value(id, np.var(mfcc_delta))
rmse_mean.set_value(id, np.mean(rmse)) # rmse
rmse_std.set_value(id, np.std(rmse))
rmse_var.set_value(id, np.var(rmse))
cent_mean.set_value(id, np.mean(cent)) # cent
cent_std.set_value(id, np.std(cent))
cent_var.set_value(id, np.var(cent))
spec_bw_mean.set_value(id, np.mean(spec_bw)) # spectral bandwidth
spec_bw_std.set_value(id, np.std(spec_bw))
spec_bw_var.set_value(id, np.var(spec_bw))
contrast_mean.set_value(id, np.mean(contrast)) # contrast
contrast_std.set_value(id, np.std(contrast))
contrast_var.set_value(id, np.var(contrast))
rolloff_mean.set_value(id, np.mean(rolloff)) # rolloff
rolloff_std.set_value(id, np.std(rolloff))
rolloff_var.set_value(id, np.var(rolloff))
poly_mean.set_value(id, np.mean(poly_features)) # poly features
poly_std.set_value(id, np.std(poly_features))
poly_var.set_value(id, np.var(poly_features))
tonnetz_mean.set_value(id, np.mean(tonnetz)) # tonnetz
tonnetz_std.set_value(id, np.std(tonnetz))
tonnetz_var.set_value(id, np.var(tonnetz))
zcr_mean.set_value(id, np.mean(zcr)) # zero crossing rate
zcr_std.set_value(id, np.std(zcr))
zcr_var.set_value(id, np.var(zcr))
harm_mean.set_value(id, np.mean(harmonic)) # harmonic
harm_std.set_value(id, np.std(harmonic))
harm_var.set_value(id, np.var(harmonic))
perc_mean.set_value(id, np.mean(percussive)) # percussive
perc_std.set_value(id, np.std(percussive))
perc_var.set_value(id, np.var(percussive))
frame_mean.set_value(id, np.mean(frames_to_time)) # frames
frame_std.set_value(id, np.std(frames_to_time))
frame_var.set_value(id, np.var(frames_to_time))
print(songname)
id = id+1
# Concatenating Features into one csv and json format
feature_set['song_name'] = songname_vector # song name
feature_set['tempo'] = tempo_vector # tempo
feature_set['total_beats'] = total_beats # beats
feature_set['average_beats'] = average_beats
feature_set['chroma_stft_mean'] = chroma_stft_mean # chroma stft
feature_set['chroma_stft_std'] = chroma_stft_std
feature_set['chroma_stft_var'] = chroma_stft_var
feature_set['chroma_cq_mean'] = chroma_cq_mean # chroma cq
feature_set['chroma_cq_std'] = chroma_cq_std
feature_set['chroma_cq_var'] = chroma_cq_var
feature_set['chroma_cens_mean'] = chroma_cens_mean # chroma cens
feature_set['chroma_cens_std'] = chroma_cens_std
feature_set['chroma_cens_var'] = chroma_cens_var
feature_set['melspectrogram_mean'] = mel_mean # melspectrogram
feature_set['melspectrogram_std'] = mel_std
feature_set['melspectrogram_var'] = mel_var
feature_set['mfcc_mean'] = mfcc_mean # mfcc
feature_set['mfcc_std'] = mfcc_std
feature_set['mfcc_var'] = mfcc_var
feature_set['mfcc_delta_mean'] = mfcc_delta_mean # mfcc delta
feature_set['mfcc_delta_std'] = mfcc_delta_std
feature_set['mfcc_delta_var'] = mfcc_delta_var
feature_set['rmse_mean'] = rmse_mean # rmse
feature_set['rmse_std'] = rmse_std
feature_set['rmse_var'] = rmse_var
feature_set['cent_mean'] = cent_mean # cent
feature_set['cent_std'] = cent_std
feature_set['cent_var'] = cent_var
feature_set['spec_bw_mean'] = spec_bw_mean # spectral bandwidth
feature_set['spec_bw_std'] = spec_bw_std
feature_set['spec_bw_var'] = spec_bw_var
feature_set['contrast_mean'] = contrast_mean # contrast
feature_set['contrast_std'] = contrast_std
feature_set['contrast_var'] = contrast_var
feature_set['rolloff_mean'] = rolloff_mean # rolloff
feature_set['rolloff_std'] = rolloff_std
feature_set['rolloff_var'] = rolloff_var
feature_set['poly_mean'] = poly_mean # poly features
feature_set['poly_std'] = poly_std
feature_set['poly_var'] = poly_var
feature_set['tonnetz_mean'] = tonnetz_mean # tonnetz
feature_set['tonnetz_std'] = tonnetz_std
feature_set['tonnetz_var'] = tonnetz_var
feature_set['zcr_mean'] = zcr_mean # zero crossing rate
feature_set['zcr_std'] = zcr_std
feature_set['zcr_var'] = zcr_var
feature_set['harm_mean'] = harm_mean # harmonic
feature_set['harm_std'] = harm_std
feature_set['harm_var'] = harm_var
feature_set['perc_mean'] = perc_mean # percussive
feature_set['perc_std'] = perc_std
feature_set['perc_var'] = perc_var
feature_set['frame_mean'] = frame_mean # frames
feature_set['frame_std'] = frame_std
feature_set['frame_var'] = frame_var
# Converting Dataframe into CSV Excel and JSON file
feature_set.to_csv('Emotion_features.csv')
feature_set.to_json('Emotion_features.json')
# Extracting Feature Function Call
extract_feature('Dataset/')
================================================
FILE: SourceCode/HyperparamaterTuning.py
================================================
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.grid_search import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
data = pd.read_csv('Dataset/Emotion_data.csv')
X = data.ix[:, 'tempo':]
y = data['class']
featureName = list(X)
for name in featureName:
X[name] = (X[name]-X[name].min())/(X[name].max()-X[name].min())
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=26)
knn = KNeighborsClassifier()
param_grid = { 'n_neighbors': np.arange(1, 25) }
knn_cv = GridSearchCV(knn, param_grid, cv=10)
knn_cv.fit(X_train, y_train)
print(knn_cv.best_params_)
print("Baseline Accuracy: "),
print(knn_cv.best_score_)
y_pred = knn_cv.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print("Testing Accuracy: "),
print(accuracy_score(y_test, y_pred))
================================================
FILE: SourceCode/ScatterPlotDistribution.py
================================================
"""
@author: Danyal
The following code plots a scatter plot for distribution of features
against emotional classes
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
labels = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
plt.style.use('ggplot')
for label in feature:
plt.figure(figsize=(12,12))
plt.xlabel('Class')
plt.ylabel(label)
plt.title(label + ' Distribution')
plt.scatter(data['label'], feature[label], color=color)
plt.savefig('Figure\\ScatterPlot\\' + label)
plt.show()
plt.clf()
================================================
FILE: SourceCode/ScatterPlotNormalizedDistribution.py
================================================
"""
@author: Danyal
The following code plots a scatter plot for distribution
of features against emotional classes (normalized values)
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
labels = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
plt.style.use('ggplot')
for label in feature:
# Normalization (value-mean)/(max-min)
feature[label] = np.abs(feature[label]-feature[label].mean())/(feature[label].max()-feature[label].min()).astype(np.float64)
plt.figure(figsize=(12,12))
plt.xlabel('Class')
plt.ylabel(label)
plt.title(label + ' Distribution')
plt.scatter(data['label'], feature[label], color=color)
plt.savefig('Figure\\ScatterPlot\\Normalized\\' + label)
plt.show()
plt.clf()
================================================
FILE: SourceCode/SingleFeaturekNN.py
================================================
"""
@author: Danyal
The following code classifies piece of music as one of
the four emotions mentioned in the document
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
featureName = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
plt.style.use('ggplot')
array = np.array(data)
for iterator in range(4, len(array)):
features = array[:, iterator]
# Normalization
features = np.abs(features-features.mean())/(features.max()-features.min())
labels = data.ix[:, 'class'].dropna()
test_size = 0.20
random_seed = 7
train_d, test_d, train_l, test_l = train_test_split(features, labels, test_size=test_size, random_state=random_seed)
train_d = train_d.reshape(-1, 1)
train_l = train_l.reshape(-1, 1)
test_d = test_d.reshape(-1, 1)
test_l = test_l.reshape(-1, 1)
result = []
xlabel = [i for i in range(1, 11)]
for neighbors in range(1, 11):
kNN = KNeighborsClassifier(n_neighbors=neighbors)
kNN.fit(train_d, train_l)
prediction = kNN.predict(test_d)
result.append(accuracy_score(prediction, test_l)*100)
plt.figure(figsize=(10, 10))
plt.xlabel('kNN Neighbors for k=1,2...10')
plt.ylabel('Accuracy Score')
plt.title('kNN Classifier Result for ' + featureName[iterator])
plt.ylim(0, 100)
plt.xlim(0, xlabel[len(xlabel)-1]+1)
plt.plot(xlabel, result)
plt.savefig('Figure\\Individual\\Normalized\\' + featureName[iterator] + '.png')
plt.show()
================================================
FILE: SourceCode/ViolinAndStripSubplot.py
================================================
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
target = data['label']
targetName = data['class']
featureName = list(feature)
for name in featureName:
plt.figure(figsize=(12, 12))
plt.subplot(2,1,1)
sns.stripplot(x='class', y=name, data=data, jitter=True)
plt.title('Strip Plot for ' + name)
plt.subplot(2,1,2)
sns.violinplot(x='class', y=name, data=data)
plt.title('Violin Plot for ' + name)
plt.tight_layout()
plt.savefig('Plots\\Violin and Strip Subplot\\' + name)
plt.show()
plt.clf()
================================================
FILE: SourceCode/ViolinStripAndMixPlot.py
================================================
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
target = data['label']
targetName = data['class']
featureName = list(feature)
for name in featureName:
plt.figure(figsize=(12, 12))
sns.stripplot(x='class', y=name, data=data, jitter=True)
plt.title('Strip Plot for ' + name)
plt.savefig('Plots\\Strip Plot\\' + name)
plt.show()
plt.clf()
plt.figure(figsize=(12, 12))
sns.violinplot(x='class', y=name, data=data)
plt.title('Violin Plot for ' + name)
plt.savefig('Plots\\Violin Plot\\' + name)
plt.show()
plt.clf()
plt.figure(figsize=(12, 12))
sns.violinplot(x='class', y=name, data=data, inner=None, color='lightgray')
sns.stripplot(x='class', y=name, data=data, jitter=True)
plt.title('Violin and Strip Plot for ' + name)
plt.savefig('Plots\\Violin and Strip Plot\\' + name)
plt.show()
plt.clf()
================================================
FILE: SourceCode/Visualization.py
================================================
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv('Dataset/Emotion_data.csv')
feature = data.ix[:, 'tempo':]
target = data['label']
targetName = data['class']
featureName = list(feature)
color = ['red' if l==1 else 'green' if l==2 else 'blue' if l==3 else 'orange' for l in data['label']]
for name in featureName:
feature[name] = (feature[name]-feature[name].min())/(feature[name].max()-feature[name].min())
feature['class'] = data['class']
feature_mean = feature.ix[:, 'chroma_stft_mean'::3]
feature_std = feature.ix[:, 'chroma_stft_var'::3]
feature_var = feature.ix[:, 'chroma_stft_std'::3]
fig, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(feature[feature['class']=='sad'].ix[:, :-1], linewidths=0.5, ax=ax, cmap='Greens')
plt.show()
gitextract_nu0kbpz2/
├── Emotion_features.csv
├── Feature-Extraction.py
├── README.md
└── SourceCode/
├── Emotion-Recognition-RandomSeed.py
├── Emotion-Recognition.py
├── Feature-Extraction.py
├── HyperparamaterTuning.py
├── ScatterPlotDistribution.py
├── ScatterPlotNormalizedDistribution.py
├── SingleFeaturekNN.py
├── ViolinAndStripSubplot.py
├── ViolinStripAndMixPlot.py
└── Visualization.py
SYMBOL INDEX (2 symbols across 2 files) FILE: Feature-Extraction.py function extract_feature (line 16) | def extract_feature(path): FILE: SourceCode/Feature-Extraction.py function extract_feature (line 16) | def extract_feature(path):
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (86K chars).
[
{
"path": "Emotion_features.csv",
"chars": 51640,
"preview": "id,song_name,class,label,tempo,total_beats,average_beats,chroma_stft_mean,chroma_stft_std,chroma_stft_var,chroma_cq_mean"
},
{
"path": "Feature-Extraction.py",
"chars": 10149,
"preview": "import librosa\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom os import listdir\r\nfrom os.path import isfile, join\r\n\r\n'''"
},
{
"path": "README.md",
"chars": 1499,
"preview": "# Music Emotion Recognition\n\nA video explanation of the repo and research problem domain: https://youtu.be/5b5unjdikPo\n\n"
},
{
"path": "SourceCode/Emotion-Recognition-RandomSeed.py",
"chars": 1649,
"preview": "\"\"\"\r\n@author: Danyal\r\n\r\nThe following code classifies piece of music as one of \r\nthe four emotions mentioned in the docu"
},
{
"path": "SourceCode/Emotion-Recognition.py",
"chars": 1561,
"preview": "\"\"\"\r\n@author: Danyal\r\n\r\nThe following code classifies piece of music as one of \r\nthe four emotions mentioned in the docu"
},
{
"path": "SourceCode/Feature-Extraction.py",
"chars": 10149,
"preview": "import librosa\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom os import listdir\r\nfrom os.path import isfile, join\r\n\r\n'''"
},
{
"path": "SourceCode/HyperparamaterTuning.py",
"chars": 1038,
"preview": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.grid_searc"
},
{
"path": "SourceCode/ScatterPlotDistribution.py",
"chars": 728,
"preview": "\"\"\"\r\n@author: Danyal\r\n\r\nThe following code plots a scatter plot for distribution of features\r\nagainst emotional classes\r"
},
{
"path": "SourceCode/ScatterPlotNormalizedDistribution.py",
"chars": 941,
"preview": "\"\"\"\r\n@author: Danyal\r\n\r\nThe following code plots a scatter plot for distribution \r\nof features against emotional classes"
},
{
"path": "SourceCode/SingleFeaturekNN.py",
"chars": 1912,
"preview": "\"\"\"\r\n@author: Danyal\r\n\r\nThe following code classifies piece of music as one of \r\nthe four emotions mentioned in the docu"
},
{
"path": "SourceCode/ViolinAndStripSubplot.py",
"chars": 697,
"preview": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\ndata = pd.read_csv('D"
},
{
"path": "SourceCode/ViolinStripAndMixPlot.py",
"chars": 1036,
"preview": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\ndata = pd.read_csv('D"
},
{
"path": "SourceCode/Visualization.py",
"chars": 835,
"preview": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\ndata = pd.read_csv('D"
}
]
About this extraction
This page contains the full source code of the danz1ka19/Music-Emotion-Recognition GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (81.9 KB), approximately 34.4k tokens, and a symbol index with 2 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.