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Pulling bird sounds out of the fog

I'm on a research visit to Rémi Gribonval's research group at INRIA (Rennes, France). So far it's been great, and maybe I'll tell you more later, but first I just want to blog a little signal-processing achievement for today.

Together with Bob Sturm and Boris Mailhé I've been working on improvements to MPTK, the C++ toolkit for sparse decomposition of signals. I've spent quite a lot of time on making a nice Python wrapper (yay) and also on code refactoring (hmm), but today I have actually done some signal processing:

Below you can see a little spectrogram of a chiffchaff singing:


Time is along the x-axis, frequency the y-axis. (The audio is from Xeno Canto: #XC25760.)

Now, in my previous research work I developed a way of tracking those chirrups, but it relied on a rather simplistic first step of detecting individual sounds. What I've been able to do, finally today, is use Matching Pursuit instead, thanks to MPTK (with the "anywave" feature which I think I have just fixed). Potentially, this has some advantages in detecting birdsong syllables cleanly.

So my first test today is to take a simple "template" for a syllable:


And use this as a one-atom dictionary in matching pursuit applied to the above spectrogram. The result is a set of "detections" which I can use for various purposes, such as, for example, reconstructing a cleaned-up spectrogram:


Looks pretty good eh? There's one false-positive in the above, and one or two false-negatives, but the basic principle is looking good. This should be useful.

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