Which algorithm should I use for signal (sound) one class classification?

Question

Update this question was previously titled as \"Give me the name of a simple algorithm for signal(sound) pattern detection\"

1. My o

Answer 1

Some more information is needed.

When you say noisy signal what is the background noise? Is it, to a first approximation, stationary (in a statistical sense, i.e. constant) or is it non-stationary (i.e. likely to contain other sounds, such as other animal calls etc?)

If the background noise is non-stationary then your best bet might be to use something called Independent Components Analysis which attempts to separate a given sound mixture into its component sources, you wouldn't even need the original recording of the insect itself. Lots of ICA software is linked from the Wikipedia page.

(Edit: ICA is a case of Blind Source Separation (BSS), there are many other ways of doing BSS and it might help to search for those as well.)

If however, the background noise is stationary then the problem is much easier (though still very hard):

In this case the approach I would use is as follows. Analyse the amplitude spectrum of a bit of the noise and the amplitude spectrum of your insect call. If you're lucky the insect call may, in general, be in a different frequency band to the noise. If so filter the incoming signal with suitable high-, low-, or band-pass filter.

You can then try comparing sections of your filtered signal that contain "more energy" than average with your (filtered) insect call. Possibly by using the image similarity algorithms suggested by A. Rex.

Edit: Since your background-noise is non-stationary then I can only suggest that searching for Blind Source Separation of non-Gaussian sources may lead you to some more algorithms. I'm afraid that the answer is that there is no simple algorithm that will do what you want.