Multi-label classification of sounds using neural networks. (Urbansound8K Dataset)
How to best generate my combined dataset, considering maximum 2 sounds combined at the same time.
Say I have 9 sounds belonging to 3 different classes (A,B,C):
A1 A2 A3
B1 B2 B3
C1 C2 C3
1) Not repeat any file. This would give 4 combinations (8 files) and 1 leftover file. For example: A1B1, A2B2, A3C2, B3C3. Leftover C1.
2) Repeat files, but only with one other example of each different class. This would give 9 combinations: A1B1, A1C1, A2B2, A2C2, A3B3, A3C3, B1C1, B2C2, B3C3.
3) Repeat files as many times as there are files of other classes. For example, combining the example A1 would give: A1B1, A1B2, A1B3, A1C1, A1C2, A1C3. I believe this option would give in total 27 examples.
1) Which option would the the optimal for training a neural network for multi-label classification of the classes A,B and C?
2) Is there any library that would make this automatically?
Thanks for any help!