- There are samples of audio files, classified according to their genre.
- All genres have 100 samples.
In GTZAN dataset there are two CSV files with features:
- features_30_sec.csv
- features_3_sec.csv
We'll train on these two sets to observe differences:
First test was done on 30 seconds of samples.
Training details
Epoch | Train Loss | Valid Loss | Accuracy | Time |
---|---|---|---|---|
0 | 2.410511 | 2.301348 | 0.100000 | 00:00 |
1 | 2.358546 | 2.294634 | 0.150000 | 00:00 |
2 | 2.308661 | 2.264426 | 0.200000 | 00:00 |
3 | 2.234084 | 2.203711 | 0.237500 | 00:00 |
4 | 2.171810 | 2.121932 | 0.243750 | 00:00 |
5 | 2.107840 | 2.050719 | 0.256250 | 00:00 |
6 | 2.039669 | 1.963892 | 0.281250 | 00:00 |
7 | 1.977348 | 1.895904 | 0.306250 | 00:00 |
8 | 1.910779 | 1.822734 | 0.331250 | 00:00 |
9 | 1.841186 | 1.764186 | 0.337500 | 00:00 |
10 | 1.774352 | 1.701079 | 0.356250 | 00:00 |
11 | 1.703704 | 1.629614 | 0.356250 | 00:00 |
12 | 1.633385 | 1.574726 | 0.387500 | 00:00 |
13 | 1.564862 | 1.509628 | 0.412500 | 00:00 |
14 | 1.501113 | 1.446676 | 0.475000 | 00:00 |
15 | 1.449700 | 1.387999 | 0.500000 | 00:00 |
16 | 1.374840 | 1.333856 | 0.575000 | 00:00 |
17 | 1.309132 | 1.269413 | 0.606250 | 00:00 |
18 | 1.251293 | 1.207156 | 0.625000 | 00:00 |
19 | 1.191447 | 1.157811 | 0.643750 | 00:00 |
20 | 1.127759 | 1.115797 | 0.650000 | 00:00 |
21 | 1.067127 | 1.049386 | 0.687500 | 00:00 |
22 | 1.009375 | 1.008271 | 0.706250 | 00:00 |
23 | 0.951300 | 0.977570 | 0.718750 | 00:00 |
24 | 0.893850 | 0.922465 | 0.731250 | 00:00 |
25 | 0.838244 | 0.878590 | 0.712500 | 00:00 |
26 | 0.787135 | 0.868914 | 0.725000 | 00:00 |
27 | 0.732795 | 0.853740 | 0.737500 | 00:00 |
28 | 0.678135 | 0.883555 | 0.693750 | 00:00 |
29 | 0.625145 | 0.845650 | 0.706250 | 00:00 |
30 | 0.583684 | 0.802656 | 0.725000 | 00:00 |
31 | 0.532835 | 0.794608 | 0.731250 | 00:00 |
32 | 0.489864 | 0.795410 | 0.743750 | 00:00 |
33 | 0.443835 | 0.774997 | 0.743750 | 00:00 |
34 | 0.409310 | 0.803072 | 0.725000 | 00:00 |
35 | 0.375299 | 0.759579 | 0.731250 | 00:00 |
36 | 0.340224 | 0.802695 | 0.743750 | 00:00 |
37 | 0.306562 | 0.796328 | 0.743750 | 00:00 |
38 | 0.280238 | 0.809968 | 0.750000 | 00:00 |
39 | 0.254191 | 0.783211 | 0.743750 | 00:00 |
40 | 0.228860 | 0.777353 | 0.762500 | 00:00 |
41 | 0.208046 | 0.811578 | 0.725000 | 00:00 |
42 | 0.186007 | 0.822938 | 0.731250 | 00:00 |
43 | 0.170087 | 0.735695 | 0.737500 | 00:00 |
44 | 0.159519 | 0.791559 | 0.743750 | 00:00 |
45 | 0.149130 | 0.859067 | 0.718750 | 00:00 |
46 | 0.136737 | 0.820262 | 0.762500 | 00:00 |
47 | 0.122899 | 0.873859 | 0.743750 | 00:00 |
48 | 0.116056 | 0.791122 | 0.750000 | 00:00 |
49 | 0.107419 | 0.854898 | 0.706250 | 00:00 |
50 | 0.100285 | 0.879887 | 0.718750 | 00:00 |
51 | 0.089521 | 0.862034 | 0.743750 | 00:00 |
52 | 0.081394 | 0.828892 | 0.737500 | 00:00 |
53 | 0.073154 | 0.887935 | 0.743750 | 00:00 |
And classification report results:
Class | Precision | Recall | F1-Score | Support |
---|---|---|---|---|
0 (blues) | 0.80 | 0.80 | 0.80 | 20 |
1 (classical) | 0.86 | 1.00 | 0.92 | 12 |
2 (country) | 0.60 | 0.75 | 0.67 | 16 |
3 (disco) | 0.67 | 0.67 | 0.67 | 18 |
4 (hiphop) | 0.92 | 0.55 | 0.69 | 22 |
5 (jazz) | 0.94 | 0.71 | 0.81 | 21 |
6 (metal) | 0.87 | 0.93 | 0.90 | 14 |
7 (pop) | 0.65 | 0.85 | 0.73 | 13 |
8 (reggae) | 0.46 | 0.67 | 0.55 | 9 |
9 (rock) | 0.71 | 0.67 | 0.69 | 15 |
Accuracy | 0.74 | 160 | ||
Macro Avg | 0.75 | 0.76 | 0.74 | 160 |
Weighted Avg | 0.77 | 0.74 | 0.74 | 160 |
Running the training a few more times yielded results between accuracies of 70% to 74%.
Second test was done on 3 seconds of samples.
Training details
Epoch | Train Loss | Valid Loss | Accuracy | Time |
---|---|---|---|---|
0 | 2.032246 | 1.873680 | 0.360451 | 00:01 |
1 | 1.751166 | 1.637501 | 0.448686 | 00:01 |
2 | 1.570411 | 1.483692 | 0.508761 | 00:01 |
3 | 1.430684 | 1.365427 | 0.556320 | 00:01 |
4 | 1.307044 | 1.269431 | 0.596370 | 00:01 |
5 | 1.219212 | 1.175792 | 0.637672 | 00:01 |
6 | 1.138508 | 1.086589 | 0.667710 | 00:01 |
7 | 1.026570 | 1.008204 | 0.693367 | 00:01 |
8 | 0.954476 | 0.930218 | 0.705882 | 00:01 |
9 | 0.889936 | 0.864621 | 0.731539 | 00:01 |
10 | 0.815705 | 0.815270 | 0.740926 | 00:01 |
11 | 0.761109 | 0.770075 | 0.759700 | 00:01 |
12 | 0.719386 | 0.718789 | 0.777847 | 00:01 |
13 | 0.663675 | 0.683248 | 0.783479 | 00:01 |
14 | 0.603862 | 0.653833 | 0.793492 | 00:01 |
15 | 0.555469 | 0.619850 | 0.801627 | 00:01 |
16 | 0.506831 | 0.575025 | 0.820400 | 00:01 |
17 | 0.471152 | 0.554522 | 0.825407 | 00:01 |
18 | 0.433732 | 0.531214 | 0.836045 | 00:01 |
19 | 0.416024 | 0.520105 | 0.828536 | 00:01 |
20 | 0.362679 | 0.504946 | 0.831039 | 00:01 |
21 | 0.333553 | 0.481875 | 0.839800 | 00:01 |
22 | 0.313346 | 0.454316 | 0.848561 | 00:01 |
23 | 0.295104 | 0.459885 | 0.836671 | 00:01 |
24 | 0.260602 | 0.424484 | 0.864205 | 00:01 |
25 | 0.246882 | 0.449828 | 0.852941 | 00:01 |
26 | 0.212048 | 0.420795 | 0.857322 | 00:01 |
27 | 0.194751 | 0.419301 | 0.862328 | 00:01 |
28 | 0.193910 | 0.427121 | 0.860451 | 00:01 |
29 | 0.177843 | 0.416969 | 0.860451 | 00:01 |
30 | 0.161258 | 0.408608 | 0.875469 | 00:01 |
31 | 0.168606 | 0.395215 | 0.866083 | 00:01 |
32 | 0.144902 | 0.402985 | 0.866083 | 00:01 |
33 | 0.127127 | 0.395520 | 0.867334 | 00:01 |
34 | 0.130257 | 0.389912 | 0.882979 | 00:01 |
35 | 0.136157 | 0.402041 | 0.869837 | 00:01 |
36 | 0.128909 | 0.403417 | 0.877347 | 00:01 |
37 | 0.124474 | 0.409458 | 0.867334 | 00:01 |
38 | 0.112614 | 0.412239 | 0.881727 | 00:01 |
39 | 0.120688 | 0.421026 | 0.873592 | 00:01 |
40 | 0.101785 | 0.377680 | 0.886733 | 00:01 |
41 | 0.107204 | 0.402054 | 0.880476 | 00:01 |
42 | 0.104357 | 0.389439 | 0.877347 | 00:01 |
43 | 0.101453 | 0.407585 | 0.881101 | 00:01 |
44 | 0.088406 | 0.386060 | 0.884230 | 00:01 |
45 | 0.090783 | 0.396249 | 0.880476 | 00:01 |
46 | 0.082592 | 0.372472 | 0.879850 | 00:01 |
47 | 0.086191 | 0.373930 | 0.894243 | 00:01 |
48 | 0.081868 | 0.362440 | 0.889862 | 00:01 |
49 | 0.065329 | 0.384066 | 0.882353 | 00:01 |
50 | 0.083963 | 0.380745 | 0.889862 | 00:01 |
51 | 0.082650 | 0.417959 | 0.889862 | 00:01 |
52 | 0.075654 | 0.388021 | 0.892365 | 00:01 |
53 | 0.080464 | 0.403859 | 0.891740 | 00:01 |
54 | 0.063071 | 0.371227 | 0.891114 | 00:01 |
55 | 0.073778 | 0.353450 | 0.899249 | 00:01 |
56 | 0.073829 | 0.391176 | 0.884230 | 00:01 |
57 | 0.064231 | 0.365528 | 0.894243 | 00:01 |
58 | 0.062055 | 0.373422 | 0.895494 | 00:01 |
59 | 0.063687 | 0.391158 | 0.893617 | 00:01 |
60 | 0.061825 | 0.417160 | 0.882979 | 00:01 |
61 | 0.052974 | 0.394985 | 0.903004 | 00:01 |
62 | 0.063245 | 0.395809 | 0.887985 | 00:01 |
63 | 0.054693 | 0.358832 | 0.903004 | 00:01 |
64 | 0.052507 | 0.388410 | 0.895494 | 00:01 |
65 | 0.052654 | 0.352484 | 0.906758 | 00:01 |
66 | 0.051661 | 0.413555 | 0.892991 | 00:01 |
67 | 0.056109 | 0.394127 | 0.895494 | 00:01 |
68 | 0.056794 | 0.372550 | 0.903004 | 00:01 |
69 | 0.058300 | 0.374250 | 0.896746 | 00:01 |
70 | 0.046908 | 0.352735 | 0.909262 | 00:01 |
71 | 0.054862 | 0.368875 | 0.904881 | 00:01 |
72 | 0.042489 | 0.400974 | 0.891740 | 00:01 |
73 | 0.045906 | 0.384763 | 0.897372 | 00:01 |
74 | 0.053988 | 0.396204 | 0.896746 | 00:01 |
75 | 0.042612 | 0.384841 | 0.898623 | 00:01 |
And classification report results:
Class | Precision | Recall | F1-Score | Support |
---|---|---|---|---|
0 (blues) | 0.94 | 0.89 | 0.91 | 163 |
1 (classical) | 0.91 | 0.93 | 0.92 | 146 |
2 (country) | 0.82 | 0.82 | 0.82 | 161 |
3 (disco) | 0.84 | 0.93 | 0.88 | 168 |
4 (hiphop) | 0.94 | 0.91 | 0.93 | 162 |
5 (jazz) | 0.91 | 0.91 | 0.91 | 151 |
6 (metal) | 0.95 | 0.94 | 0.94 | 172 |
7 (pop) | 0.93 | 0.90 | 0.92 | 150 |
8 (reggae) | 0.93 | 0.90 | 0.91 | 163 |
9 (rock) | 0.82 | 0.86 | 0.84 | 162 |
Accuracy | 0.90 | 1598 | ||
Macro Avg | 0.90 | 0.90 | 0.90 | 1598 |
Weighted Avg | 0.90 | 0.90 | 0.90 | 1598 |
Running the training a few more times yielded results between accuracies around 90%.
TODO