Audio Separation of .wav signal [duplicate]

Question

What I am attempting to do is take a signal (.wav audio file) which contains calls from different Bat species. Here is a graph representation of the signal:

What I have done so far is:

1) Emphasise the signal

2) Compress the signal

This is the output I got plotted:

I need to remove the signals that do not warrent consideration. At the moment, the signal is in a 1D representation, and, after the Separation has been completed the signal will be a 2D representation containing JUST the calls, thus I can perform analysis on each of the calls.

This is my suggested methodology to do this:

1) Split the signal into blocks of: 256x100 (so they overlap)

2) Calculate the RMS (Root-mean-square) of each of the blocks

3) Compare (2) to a threshold value, and, if the block warrants consideration then this can be classed as a call.

But the problem is this:

1) Will splitting the signal into blocks of 256x100 go over the calls and thus making some of the calls lost when calculating the RMS?

2) How do I therefore pick a suitable threshold value? Would finding the peaks in the signal be an accurate assumption for this?

I hope someone can help me :)

Answer 1

If you are using Matlab, the plots looks like Matlab, you can use indexing directly to get a threshold value:

% 'time' is the time series
% 'signal' is the signal itself
idx = signal > 0.2;
plot(time(idx), signal(idx));

You are not clear on what you're trying to achieve, but there could be some information found in the frequency spectrum, so that you could do for example a fourier transform on the signal.

Answer 2

This sounds like you are trying to separate noise from signal, and subsequently separate various mixed signals. This is a good candidate for independent component analysis (ICA) with a caveat. It is unclear how you got from your first signal to the second, and it is unclear if the bat signals are individual chirps/bleats at one time point or if they constitute a time series of various magnitudes and durations. If the the latter, you can use ICA to decompose the spectrogram of the original mixed signal (your figure 1). I doubt RMS of overlapping windows will give you good signal indicators unless you are simply looking to characterize particular magnitudes of very small duration.