Hello all !! I need your help to tackle this particular problem statement I want to solve:

Suppose we have to devise an algorithm to classify sources of underwater acoustic signals recorded from a single channel hydrophone. A single recording can have different types/classes of sounds along with background noise and there can be multiple classes present in an overlapping or non overlapping fashion. So basically I need to identify what part of a recording has what class/classes present in there. Examples of different possible classes: Oil tanker, passenger ship, Whale/ sea mammal, background noise etc..

I have a rough idea about what to do, but due to lack of guidance I am not sure I am on the right path. As of now I am experimenting with clustering, feature construction such as spectrograms, mfcc, cqt etc. and then I plan to feed them to some CNN architecture. I am not sure how to handle overlapping classes. Also should I pre-process the audio but how, I might lose information ?? Please just tell me whatever you think can help.

If anyone has some experience in tackling these type of problems, can you please help me. Suggest me some ideas. Also, if anyone has some dataset of underwater acoustics, can they please share them, I will follow your rules regarding the dataset.