Classification of non-stationary acoustic signals I'm currently working on a task of classification of short non-stationary audio signals with length of 1024 elements and sampled at 120 kHz. 

I was wondering if there are any special techniques or models for classification of such signals. As far as I know, most of solution for this kind of tasks relies on computation of FFT/MFCC or  spectrograms. But due to  non-stationary nature of features, I can't compute statisticaly reliable frequency based features.

Also approach with CNN as described in the article Raw Waveform-based Audio Classification Using Sample-level CNN Architectures shows promising results, but still doesn't work good enough.

Can anyone advice me another approaches or thoughts where to start from? Will appreciate any help!
As you try to classify sequential data, you can try simple recurrent neural network or their advance version LSTMs - but as you have a short signal, RNN should work fine. 

You can read this paper on sound classification with LSTM and this  medium article