On February 18, 2016 at 10:48:20 pm +01:00, Ethan Duni <<ethan.d...@gmail.com>> wrote:
> I was kind of hoping someone would chime in with a reference to a publication > of some tests comparing different spectral centroid methods, showing how well > they match some subjective ratings of "brightness" or whatever, for various > signal classes. This doesn't seem particularly difficult, although it > requires pinning down exactly what we want these things to do. And, yes, > subjective testing, statistics, etc. I've noticed in my (cursory) searches > that some people use amplitude spectra and others use power spectra, but the > only thing I've found in the way of comparison tests was to do with whether > it gets normalized by fundamental frequency or not. > > > I'm not a partisan for any particular definition, just want to understand how > the various statistics stack up. > There is this paper about the timbre toolbox, <http://www.mcgill.ca/mpcl/files/mpcl/peeters_2011_jasa.pdf>, which discusses lots of signal descriptors. As they suggest, you could calculate spectral descriptors from the amplitude or power spectrum, from sinusoidal partials, or from Equivalent Rectangular Bandwidths for a more perceptually motivated representation. I don't recall having seen the spectral centroid being normalized to fundamental frequency in the literature, although in some cases it would make sense to normalize it to the sampling frequency so that its range is [0,1]. One could use the time domain crest factor as a measure related to brightness independently of fundamental frequency. Several other descriptors seem to be quite similar to the centroid, such as spectral slope, spectral roll-off or zero crossing rate. I can think of applications such as perceptual research where their differences matter a great deal, and other applications where you would just pick the descriptor that is most mathematically elegant or easy to implement. Risto Holopainen
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