paco:cbmi18 [Music Representations Team]

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Musical Gesture Recognition Using Machine Learning and Audio Descriptors

Paul Best, Jean Bresson, Diemo Schwarz
Submitted poster at CBMI'18, International Conference on Content-Based Multimedia Indexing

This page presents the complete results from the experiments presented in the paper.

Training and testing with 1 descriptor

The table below presents the performance of XMM models trained with only one descriptor.
The models were tested with 10 hidden states and (0.1 0.05) as regularization values.

Descriptor	Test set accuracy	Training set accuracy
Coeff. MFCC #1	0.20785819	0.29530075
Coeff. MFCC #2	0.24002193	0.28645572
Coeff. MFCC #3	0.243019	0.3143066
Coeff. MFCC #4	0.16776316	0.26617587
Coeff. MFCC #5	0.10829678	0.23765664
Coeff. MFCC #6	0.12408626	0.23354219
Coeff. MFCC #7	0.1941886	0.29765037
Coeff. MFCC #8	0.11962719	0.19603175
Coeff. MFCC #9	0.13461258	0.22757937
Coeff. MFCC #10	0.15080409	0.22455096
Coeff. MFCC #11	0.15157163	0.22398706
Coeff. MFCC #12	0.13442983	0.20447995
Freqency	0.22675438	0.2922619
Energy	0.0439693	0.05229741
Periodicity	0.064729534	0.12179407
AC1 ¹⁾	0.038121347	0.0641604
Loundess	0.23516082	0.30132625
Centroid	0.20946637	0.34698203
Spread	0.21717836	0.34344193
Skewness	0.1439693	0.22039473
Kurtosis	0.11140351	0.17880117

Training and testing with combinations of 2 descriptors

The table below presents the performance of XMM models trained with combinations of 2 audio descriptors.
The models were tested with 10 hidden states and (0.1 0.05) as regularization values.

Numeric notation used for descriptors :

[0-11] : MFCC Coefficients
[12-20] : Frequency, Energy, Periodicity, AC1, Loudness, Centroid, Spread, Skewness, Kurtosis

Descriptors	Test set accuracy	Training set accuracy
0 1	0.4263889	0.64530075
0 2	0.31944445	0.5430138
0 3	0.4434576	0.6631683
0 4	0.2886696	0.52935464
0 5	0.4060307	0.59417296
0 6	0.33088452	0.55374897
0 7	0.39342105	0.5989662
0 8	0.39967105	0.6162072
0 9	0.42339182	0.6078634
0 10	0.41217107	0.5727966
0 11	0.39320177	0.5871136
0 12	0.39312866	0.6008041
0 13	0.28351608	0.41594613
0 14	0.39152047	0.5359545
0 15	0.31100145	0.44684628
0 16	0.4168494	0.5864453
0 17	0.43165204	0.60970134
0 18	0.3938231	0.6137949
0 19	0.41476607	0.5662281
0 20	0.3756579	0.5662281
1 2	0.3941155	0.6066625
1 3	0.40434942	0.63168335
1 4	0.39283624	0.6001984
1 5	0.40650585	0.59429825
1 6	0.40336257	0.6149332
1 7	0.36136696	0.6036863
1 8	0.35650584	0.60718465
1 9	0.43165204	0.5757936
1 10	0.35201022	0.57103175
1 11	0.38399124	0.6168233
1 12	0.47207603	0.6387218
1 13	0.28739035	0.4332498
1 14	0.43622077	0.5983187
1 15	0.31114766	0.49738932
1 16	0.47404972	0.63402254
1 17	0.38490498	0.5989244
1 18	0.46381578	0.64654345
1 19	0.36282894	0.57943815
1 20	0.36195177	0.5431391
2 3	0.3232456	0.5353592
2 4	0.2847222	0.5092523
2 5	0.32810673	0.5585526
2 6	0.29038742	0.5448935
2 7	0.29027778	0.51158107
2 8	0.27986112	0.53714496
2 9	0.32284358	0.52642024
2 10	0.26875	0.48665413
2 11	0.31103802	0.5442878
2 12	0.4119152	0.5954261
2 13	0.25314328	0.4057853
2 14	0.28501463	0.49788013
2 15	0.26980993	0.40582708
2 16	0.39312866	0.5906955
2 17	0.41787282	0.6560777
2 18	0.45489767	0.64714915
2 19	0.33665934	0.5330096
2 20	0.3096491	0.5056913
3 4	0.24539474	0.5057018
3 5	0.25782165	0.47225356
3 6	0.30745614	0.5234858
3 7	0.29206872	0.48433584
3 8	0.24312866	0.4961362
3 9	0.2699196	0.44088346
3 10	0.3030702	0.50393695
3 11	0.2690424	0.5133772
3 12	0.3030702	0.51280284
3 13	0.20003656	0.32439432
3 14	0.24214182	0.43970343
3 15	0.20657894	0.37192982
3 16	0.43293127	0.606746
3 17	0.36016083	0.5336571
3 18	0.4150585	0.62102134
3 19	0.2736111	0.49083126
3 20	0.22467105	0.4522243
4 5	0.24758773	0.48847118
4 6	0.28611112	0.46521512
4 7	0.21710527	0.48133877
4 8	0.18841374	0.43016917
4 9	0.25621346	0.4830514
4 10	0.23505117	0.445071
4 11	0.23230994	0.48011696
4 12	0.3449927	0.54486216
4 13	0.13062866	0.30956557
4 14	0.22306288	0.40111738
4 15	0.1622076	0.35050124
4 16	0.4057383	0.5602861
4 17	0.3349415	0.568066
4 18	0.39510235	0.5871867
4 19	0.2918494	0.47947994
4 20	0.2302266	0.40701753
5 6	0.34645468	0.50982666
5 7	0.23855995	0.5002297
5 8	0.22722954	0.4818609
5 9	0.24312866	0.4307853
5 10	0.20599416	0.4509712
5 11	0.23190789	0.45098162
5 12	0.29663742	0.5227757
5 13	0.12916667	0.31429616
5 14	0.16885965	0.39813074
5 15	0.21016082	0.3689745
5 16	0.40621346	0.59181285
5 17	0.33483186	0.49913326
5 18	0.35899124	0.5923977
5 19	0.21483918	0.44627193
5 20	0.23618421	0.4224833
6 7	0.21027047	0.49375522
6 8	0.2758772	0.4652047
6 9	0.31878656	0.4677005
6 10	0.2381579	0.50149334
6 11	0.24261697	0.500919
6 12	0.32284358	0.5460318
6 13	0.2424342	0.35944027
6 14	0.27569443	0.44911236
6 15	0.25453216	0.39570802
6 16	0.32452485	0.5062552
6 17	0.37050438	0.5698204
6 18	0.39184943	0.6281224
6 19	0.29762426	0.47361112
6 20	0.24144738	0.4034461
7 8	0.21980994	0.465236
7 9	0.19477339	0.44684628
7 10	0.20698099	0.45513785
7 11	0.23687865	0.42543858
7 12	0.26546052	0.49490392
7 13	0.16330409	0.28107768
7 14	0.17342836	0.38495195
7 15	0.1622076	0.31551796
7 16	0.36849415	0.57637847
7 17	0.29097223	0.5086988
7 18	0.36224416	0.5371658
7 19	0.23062866	0.4462406
7 20	0.20957603	0.39448622
8 9	0.18464913	0.395165
8 10	0.1941886	0.4254908
8 11	0.18424708	0.40580618
8 12	0.30716375	0.50075186
8 13	0.11396199	0.27444655
8 14	0.12975146	0.34225145
8 15	0.09689327	0.28816834
8 16	0.38399124	0.5924499
8 17	0.29923245	0.510401
8 18	0.3436038	0.5739557
8 19	0.2516813	0.3927736
8 20	0.19718567	0.3559315
9 10	0.19177632	0.41705304
9 11	0.200731	0.4473893
9 12	0.3249269	0.5085631
9 13	0.16330409	0.26504803
9 14	0.19517544	0.3588868
9 15	0.18336989	0.32086465
9 16	0.35796782	0.5859127
9 17	0.3116228	0.49920633
9 18	0.34546784	0.590048
9 19	0.21542397	0.41236424
9 20	0.16637427	0.35587928
10 11	0.24272661	0.44026732
10 12	0.29623538	0.47293234
10 13	0.17244153	0.32088554
10 14	0.2124269	0.36305347
10 15	0.20201023	0.37552214
10 16	0.37902048	0.56390977
10 17	0.32174706	0.5419695
10 18	0.35062134	0.55736214
10 19	0.1825658	0.41652048
10 20	0.15526316	0.354741
11 12	0.24809942	0.4913847
11 13	0.15548246	0.27572054
11 14	0.20639619	0.35948205
11 15	0.16688597	0.29947788
11 16	0.39210525	0.5561508
11 17	0.3311769	0.5544068
11 18	0.38358918	0.563868
11 19	0.22605995	0.39813074
11 20	0.21553363	0.38147452
12 13	0.21513158	0.38970342
12 14	0.26516813	0.43611112
12 15	0.2558114	0.3885756
12 16	0.45526317	0.65716374
12 17	0.35599417	0.54845447
12 18	0.45328948	0.62575186
12 19	0.32313597	0.5056391
12 20	0.27975145	0.46232247
13 14	0.2066886	0.24714913
13 15	0.080409356	0.13373016
13 16	0.3255117	0.4046366
13 17	0.26165935	0.3933062
13 18	0.33881578	0.48668545
13 19	0.17185673	0.27215958
13 20	0.07923976	0.19906016
14 15	0.16699562	0.25198412
14 16	0.388962	0.53189224
14 17	0.34945175	0.52765245
14 18	0.44119152	0.5645259
14 19	0.22595029	0.4105681
14 20	0.12719299	0.34281537
15 16	0.34159356	0.44505012
15 17	0.31260964	0.40579575
15 18	0.38190788	0.49975982
15 19	0.19320175	0.30777988
15 20	0.12251462	0.24415206
16 17	0.43680555	0.62981415
16 18	0.45427632	0.61908937
16 19	0.3963816	0.5751566
16 20	0.35939327	0.5556182
17 18	0.5193348	0.6892962
17 19	0.2939693	0.50273604
17 20	0.3058845	0.48013785
18 19	0.38172513	0.5858605
18 20	0.31600878	0.52052004
19 20	0.112682745	0.22402883

¹⁾ AC1: first-order autocorrelation coefficient