I am working on classifying different sounds ( not speech or words exactly something like ambulance alarm, police alarm, cough sounds etc)
I read few paper which suggested to extract dsp features such as MFccs, skewness, kurtosis, log energy, entropy, zcr etc ( using total around 20 features ) from each segmented sound event.
I am currently using all these features and training xgboost, 3 layer DNN with ReLU, I am getting good accuracy.
I also read recent deep learning papers where they just used spectrogram images and feed it to convolution network which is capturing both temporal and spatial features. ( Using spectrogram one feature only )
I am looking for some explanation which one is better method for classifying sounds and why?
Any reference paper for comparison would be additional help.