Dario's adaptive approach is interesting. It's essentially computing a frequency median, rather than a frequency mean as is the case with the derivative-power technique described in my original approach.
Dario, I would suggest experimenting with zero-phase FIR filters if you're doing offline music analysis. This would allow you to iteratively refine your median "in-place" for different points in time. – Evan Balster creator of imitone <http://imitone.com> On Wed, Feb 17, 2016 at 7:52 AM, STEFFAN DIEDRICHSEN <sdiedrich...@me.com> wrote: > This reminds me a bit of the voiced / unvoiced detection for vocoders or > level independent de-essers. It works quite well. > > > Steffan > > > > On 17.02.2016|KW7, at 13:08, Diemo Schwarz <diemo.schw...@ircam.fr> 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. > > > > _______________________________________________ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp >
_______________________________________________ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
