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 Mel Frequency cepstral coeffients and common descriptors
The table below presents the performance of XMM models trained with the 12 Mel frquency cepstral coefficients and the 9 ircam descriptors (Frequency, Energy, Periodicity, AC1, Loudness, Centroid, Spread, Skewness, and Kurtosis)
Number of hidden states | Relative regu | Absolute regu | test set accuracy | training set accuracy |
20 | 0.1 | 0.05 | 0.021052633 | 0.058803257 |
20 | 0.2 | 0.05 | 0.07046784 | 0.073694654 |
20 | 0.3 | 0.05 | 0.48687866 | 0.8526838 |
20 | 0.4 | 0.05 | 0.49937865 | 0.84136385 |
20 | 0.5 | 0.05 | 0.50423974 | 0.83066 |
30 | 0.1 | 0.05 | 0.51217103 | 0.8752297 |
30 | 0.2 | 0.05 | 0.53380847 | 0.88536966 |
30 | 0.3 | 0.05 | 0.5706506 | 0.87174184 |
30 | 0.4 | 0.05 | 0.55584795 | 0.8657477 |
30 | 0.5 | 0.05 | 0.54444445 | 0.8692982 |
40 | 0.1 | 0.05 | 0.533114 | 0.8823726 |
40 | 0.2 | 0.05 | 0.57054097 | 0.9025585 |
40 | 0.3 | 0.05 | 0.5715278 | 0.902005 |
40 | 0.4 | 0.05 | 0.5763889 | 0.8949039 |
40 | 0.5 | 0.05 | 0.5698465 | 0.881203 |
50 | 0.1 | 0.05 | 0.5335161 | 0.88831455 |
50 | 0.2 | 0.05 | 0.5601243 | 0.88062865 |
50 | 0.3 | 0.05 | 0.5486111 | 0.8776838 |
50 | 0.4 | 0.05 | 0.5541667 | 0.86996657 |
50 | 0.5 | 0.05 | 0.5483187 | 0.86873436 |
Training and testing with Mel Frequency cepstral coeffients
The table below presents the performance of XMM models trained with the 12 Mel frquency cepstral coefficients
Number of hidden states | Relative regu | Absolute regu | test set accuracy | training set accuracy |
10 | 0.1 | 0.05 | 0.4624269 | 0.7771825 |
10 | 0.15 | 0.1 | 0.44634503 | 0.79201127 |
10 | 0.05 | 0.01 | 0.43483186 | 0.7683062 |
15 | 0.1 | 0.05 | 0.49031433 | 0.8258876 |
15 | 0.15 | 0.1 | 0.5386696 | 0.82710946 |
15 | 0.05 | 0.01 | 0.44733188 | 0.82301587 |
20 | 0.1 | 0.05 | 0.53837717 | 0.84613616 |
20 | 0.15 | 0.1 | 0.5258772 | 0.85326857 |
20 | 0.05 | 0.01 | 0.4857456 | 0.85562867 |
25 | 0.1 | 0.05 | 0.5610015 | 0.85326857 |
25 | 0.15 | 0.1 | 0.5659722 | 0.87166876 |
25 | 0.05 | 0.01 | 0.55983186 | 0.8538429 |
Training and testing with common descriptors
The table below presents the performance of XMM models trained with 9 common descriptors : Frequency, Energy, Periodicity, AC1, Loudness, Centroid, Spread, Skewness, and Kurtosis
Number of hidden states | Relative regu | Absolute regu | test set accuracy | training set accuracy |
10 | 0.1 | 0.05 | 0.42372075 | 0.654198 |
10 | 0.15 | 0.1 | 0.4355263 | 0.62569964 |
10 | 0.05 | 0.01 | 0.43852338 | 0.66724104 |
15 | 0.1 | 0.05 | 0.46611843 | 0.7314745 |
15 | 0.15 | 0.1 | 0.48161548 | 0.7285192 |
15 | 0.05 | 0.01 | 0.49828216 | 0.7605785 |
20 | 0.1 | 0.05 | 0.5271564 | 0.79918546 |
20 | 0.15 | 0.1 | 0.51048977 | 0.79144735 |
20 | 0.05 | 0.01 | 0.51304824 | 0.8128655 |
25 | 0.1 | 0.05 | 0.5066155 | 0.8170008 |
25 | 0.15 | 0.1 | 0.4786184 | 0.8098684 |
25 | 0.05 | 0.01 | 0.5282529 | 0.830117 |
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 |
Training and testing with combinations of 3 descriptors
The table below presents the performance of XMM models trained with combinations of 3 audio descriptors selected from this set : (0 1 2 3 12 14 16 17 18).
The models were tested with 18 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 2 | 0.53877926 | 0.75701756 |
0 1 3 | 0.5505848 | 0.77959484 |
0 1 12 | 0.5346857 | 0.77181495 |
0 1 14 | 0.48439327 | 0.71718884 |
0 1 16 | 0.4802997 | 0.727924 |
0 1 17 | 0.463231 | 0.7076963 |
0 1 18 | 0.5342105 | 0.74685675 |
0 2 3 | 0.46480262 | 0.7475042 |
0 2 12 | 0.47335526 | 0.7338659 |
0 2 14 | 0.41546053 | 0.6595865 |
0 2 16 | 0.4572734 | 0.68871135 |
0 2 17 | 0.51604534 | 0.7243943 |
0 2 18 | 0.46659356 | 0.7010965 |
0 3 12 | 0.50252194 | 0.7582289 |
0 3 14 | 0.5197003 | 0.7510756 |
0 3 16 | 0.50292397 | 0.7249478 |
0 3 17 | 0.50679827 | 0.7415727 |
0 3 18 | 0.5313962 | 0.7486216 |
0 12 14 | 0.46451023 | 0.7017335 |
0 12 16 | 0.5565424 | 0.7278822 |
0 12 17 | 0.47108918 | 0.71013994 |
0 12 18 | 0.51820177 | 0.7219716 |
0 14 16 | 0.50303364 | 0.674363 |
0 14 17 | 0.45 | 0.68392855 |
0 14 18 | 0.4811769 | 0.6981725 |
0 16 17 | 0.4652047 | 0.71359647 |
0 16 18 | 0.5410453 | 0.71424395 |
0 17 18 | 0.4743421 | 0.7403404 |
1 2 3 | 0.52032167 | 0.78437764 |
1 2 12 | 0.48804826 | 0.7242899 |
1 2 14 | 0.4621345 | 0.72081244 |
1 2 16 | 0.5322368 | 0.7456767 |
1 2 17 | 0.5137427 | 0.7635547 |
1 2 18 | 0.55105997 | 0.77901 |
1 3 12 | 0.49597952 | 0.71129907 |
1 3 14 | 0.54035086 | 0.75884504 |
1 3 16 | 0.5623903 | 0.7599833 |
1 3 17 | 0.52090645 | 0.77901 |
1 3 18 | 0.5468933 | 0.760495 |
1 12 14 | 0.4824927 | 0.70232875 |
1 12 16 | 0.5517544 | 0.78429407 |
1 12 17 | 0.4564693 | 0.72127194 |
1 12 18 | 0.5639985 | 0.75157685 |
1 14 16 | 0.51147664 | 0.73029447 |
1 14 17 | 0.48048246 | 0.7385547 |
1 14 18 | 0.54989034 | 0.73383457 |
1 16 17 | 0.45259503 | 0.6899018 |
1 16 18 | 0.53548974 | 0.7391395 |
1 17 18 | 0.5715278 | 0.7878655 |
2 3 12 | 0.4604532 | 0.7094507 |
2 3 14 | 0.35570174 | 0.6643588 |
2 3 16 | 0.4755117 | 0.6921888 |
2 3 17 | 0.47108918 | 0.77008147 |
2 3 18 | 0.56458337 | 0.7867481 |
2 12 14 | 0.41944444 | 0.663137 |
2 12 16 | 0.49597952 | 0.7249269 |
2 12 17 | 0.44495615 | 0.6862155 |
2 12 18 | 0.520614 | 0.74446536 |
2 14 16 | 0.46878654 | 0.68865914 |
2 14 17 | 0.44674706 | 0.7260756 |
2 14 18 | 0.5483918 | 0.7469716 |
2 16 17 | 0.49269006 | 0.7095342 |
2 16 18 | 0.5140351 | 0.7106412 |
2 17 18 | 0.5531798 | 0.7843463 |
3 12 14 | 0.363231 | 0.5911967 |
3 12 16 | 0.5022295 | 0.7309106 |
3 12 17 | 0.4125 | 0.66609234 |
3 12 18 | 0.4746345 | 0.7160192 |
3 14 16 | 0.52068717 | 0.73203844 |
3 14 17 | 0.40394738 | 0.6608187 |
3 14 18 | 0.4811769 | 0.73144317 |
3 16 17 | 0.50679827 | 0.7249373 |
3 16 18 | 0.49824563 | 0.72426904 |
3 17 18 | 0.5432383 | 0.77310986 |
12 14 16 | 0.50946635 | 0.71424395 |
12 14 17 | 0.41447368 | 0.6257728 |
12 14 18 | 0.4686769 | 0.6957811 |
12 16 17 | 0.46461988 | 0.7023601 |
12 16 18 | 0.5018275 | 0.7047097 |
12 17 18 | 0.52189327 | 0.7475355 |
14 16 17 | 0.44802633 | 0.6939432 |
14 16 18 | 0.4763158 | 0.7136487 |
14 17 18 | 0.56567985 | 0.7742795 |
16 17 18 | 0.47931287 | 0.7522661 |
Confusion matrix
This confusion matrix represents the accuracy for a model tested without markers (on each frame of 100ms). This models was trained with 49 hidden states, regularization of (0.42, 0.045), with the following descriptors : Mel Frequency Cepstral Coefficients #1 #2 #3 #4 #6 #8 #12, Frequency, Energy, Periodicity, AC1, and Loudness.
Z | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10/61 | 0 | 91/122 | 0 | 0 | 0 | 11/122 | 0 |
Q | 0 | 1/32 | 1/32 | 21/32 | 3/32 | 0 | 0 | 0 | 1/32 | 0 | 3/32 | 0 | 0 | 1/32 | 0 | 0 | 0 | 0 | 1/32 | 0 |
A | 0 | 0 | 19/100 | 49/100 | 2/25 | 0 | 0 | 1/20 | 0 | 0 | 1/10 | 0 | 0 | 0 | 0 | 1/20 | 0 | 0 | 1/100 | 3/100 |
C | 0 | 1/714 | 1/34 | 201/238 | 1/119 | 1/714 | 1/714 | 29/357 | 0 | 0 | 5/357 | 0 | 0 | 2/357 | 1/357 | 5/714 | 0 | 0 | 1/357 | 0 |
B | 0 | 0 | 11/107 | 1/107 | 75/107 | 0 | 0 | 8/107 | 0 | 0 | 0 | 0 | 11/107 | 0 | 0 | 0 | 0 | 1/107 | 0 | 0 |
E | 0 | 0 | 0 | 50/201 | 4/201 | 80/201 | 10/201 | 7/67 | 0 | 0 | 1/67 | 5/201 | 1/67 | 2/201 | 0 | 0 | 0 | 1/67 | 5/67 | 5/201 |
F | 0 | 0 | 0 | 17/94 | 0 | 13/94 | 16/47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11/94 | 21/94 |
G | 0 | 3/340 | 0 | 19/340 | 0 | 3/85 | 0 | 259/340 | 1/136 | 1/340 | 13/680 | 5/136 | 0 | 0 | 3/85 | 1/68 | 0 | 0 | 0 | 3/136 |
P | 0 | 0 | 0 | 0 | 0 | 10/113 | 0 | 11/226 | 62/113 | 0 | 1/226 | 1/226 | 0 | 0 | 2/113 | 11/226 | 0 | 0 | 39/226 | 15/226 |
I | 0 | 0 | 0 | 6/29 | 0 | 0 | 0 | 33/145 | 0 | 0 | 0 | 0 | 0 | 6/145 | 0 | 0 | 0 | 0 | 0 | 76/145 |
R | 0 | 13/192 | 1/576 | 41/192 | 1/288 | 0 | 0 | 15/32 | 0 | 0 | 95/576 | 1/64 | 1/576 | 7/576 | 0 | 0 | 0 | 0 | 1/192 | 13/288 |
H | 0 | 0 | 0 | 124/673 | 0 | 10/673 | 0 | 59/673 | 58/673 | 0 | 20/673 | 289/673 | 2/673 | 69/673 | 6/673 | 7/673 | 0 | 2/673 | 13/673 | 14/673 |
J | 0 | 0 | 0 | 0 | 0 | 7/171 | 0 | 37/114 | 0 | 0 | 0 | 23/342 | 70/171 | 13/342 | 7/171 | 0 | 0 | 0 | 5/342 | 11/171 |
K | 0 | 0 | 0 | 3/40 | 0 | 0 | 0 | 3/16 | 1/160 | 0 | 0 | 5/32 | 0 | 91/160 | 1/160 | 0 | 0 | 0 | 0 | 0 |
L | 0 | 0 | 0 | 7/71 | 0 | 0 | 0 | 37/213 | 0 | 0 | 0 | 34/213 | 0 | 17/71 | 67/213 | 1/213 | 0 | 2/213 | 0 | 0 |
M | 0 | 0 | 0 | 41/125 | 0 | 4/125 | 0 | 7/125 | 0 | 0 | 13/125 | 14/125 | 0 | 0 | 0 | 39/125 | 0 | 0 | 1/125 | 6/125 |
S | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25/49 | 0 | 0 | 0 | 24/49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
N | 0 | 0 | 0 | 0 | 0 | 5/67 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39/67 | 0 | 23/67 |
O | 0 | 0 | 0 | 0 | 0 | 7/71 | 0 | 0 | 0 | 0 | 0 | 0 | 1/71 | 0 | 0 | 1/213 | 0 | 0 | 57/71 | 17/213 |
T | 0 | 1/350 | 0 | 0 | 0 | 31/350 | 0 | 3/175 | 8/175 | 3/350 | 0 | 17/350 | 0 | 1/350 | 0 | 9/175 | 0 | 0 | 19/350 | 17/25 |
Z | Q | A | C | B | E | F | G | P | I | R | H | J | K | L | M | S | N | O | T |