Comparing audio recordings

Question

I have 5 recorded wav files. I want to compare the new incoming recordings with these files and determine which one it resembles most.

In the final product I need to implement it in C++ on Linux, but now I am experimenting in Matlab. I can see FFT plots very easily. But I don't know how to compare them.

How can I compute the similarity of two FFT plots?

Edit: There is only speech in the recordings. Actually, I am trying to identify the response of answering machines of a few telecom companies. It's enough to distinguish two messages "this person can not be reached at the moment" and "this number is not used anymore"

Answer 1

This depends a lot on your definition of "resembles most". Depending on your use case this can be a lot of things. If you just want to compare the bare spectra of the whole file you can just correlate the values returned by the two ffts.

However spectra tend to change a lot when the files get warped in time. To figure out the difference with this, you need to do a windowed fft and compare the spectra for each window. This then defines your difference function you can use in a Dynamic time warping algorithm.

If you need perceptual resemblance an FFT probably does not get you what you need. An MFCC of the recordings is most likely much closer to this problem. Again, you might need to calculate windowed MFCCs instead of MFCCs of the whole recording.

If you have musical recordings again you need completely different aproaches. There is a blog posting that describes how Shazam works, so you might be able to find this on google. Or if you want real musical similarity have a look at this book

EDIT:

The best solution for the problem specified above would be the one described here ("shazam algorithm" as mentioned above).This is however a bit complicated to implement and easier solution might do well enough.

Answer 2

If you know that there are only 5 different different possible incoming files, I would suggest trying first something as easy as doing the euclidian distance between the two signals (in temporal or fourier). It is likely to give you good result.

Edit : So with different possible starts, try doing an autocorrelation and see which file has the higher peak.