Oriol Romani Picas pointed out a few errors in my implementation of the algorithm above. Thanks, Oriol!
Specifically, in step 4, the *square root* of PB/PA -- effectively an RMS -- is used, and we multiply by the sampling rate divided by two pi. The example code <http://pastebin.com/EfRv4HRC> has been updated to reflect this. Apologies for the misinformation and any confusion it might have caused -- I was referencing a very different implementation of the algorithm when writing the original post. – Evan Balster creator of imitone <http://imitone.com> On Tue, Feb 2, 2016 at 10:35 AM, Risto Holopainen <ebel...@ristoid.net> wrote: > > > On February 1, 2016 at 7:41:49 pm +01:00, Evan Balster <e...@imitone.com> > wrote: > > > > 1. Apply a first-difference filter to input signal A, yielding signal > B. > 2. Square signal A, yielding signal AA; square signal B, yielding > signal BB. > 3. Apply a low-pass filter of your choice to AA, yielding PA, and BB, > yielding PB. > 4. Divide PB by PA, then multiply the result by the input signal's > sampling rate divided by pi. > > > The low-pass filter used in step 3 determines the time-domain weighting > for the frequency average. (I recommend a rectangular or triangular > average.) > > > You don't see that formula as often as the one involving spectral bins, > but it can be found in a few places such as the DAFX book by Zölzer. It's a > nice trick when you want to track fast changes in the centroid without > having to do lots of overlapped windows. > > Another simple way if you do an FFT would be to accumulate the amplitude > of successive bins, counting from 0 Hz upwards as well as from f_s/2 > downwards, stopping at the bin where the summed amplitudes match. > > And welcome to the list! > > Risto Holopainen > > > > _______________________________________________ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp >
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