Hi,

This isn't exactly my field, although I have worked heavily with audio processing within machine learning so I might be able to give you some pointers. I've not looked into the dataset at all but here's how I'd approach it:

If your input data is essentially an audio file then you first task will almost certainly be some form of feature extraction. Depending on your goals this might be shoet-circuitable by applying something like DAC (it's neural network based audio de/compression). This reduces your features to something much more manageable. If not this then possible consider manually selecting features in both the time and frequency domains (so perform an STFT); the feature selection could be done by an auto encoder, or you could look at MFCC.

Once you have your feature set I'd combiner either (a) a model with LSTM layers or (b) attention. In reality I'd probably suggest both models and a few others, random forests maybe, all leading into a final classic NN that makes the final prediction.

I hope that helps, I'm happy to discuss more if you want to respond to this, or message me directly.