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Re: Robust method of fundamental frequency estimation.
However, what I said about the equivalence of applying a summary
autocorrelation to the output of a orthonormal linear filterbank and
applying autocorrelation to the input signal is still true, it just does
not apply to your model, because of the HWR.
That's true, but I wouldn't assume that people doing filterbank ->
autocorrelation -> summary are simply misguided. Very often in
addition to nonlinearity there is per-channel normalization (and/or
weighting to emphasize particular subbands/sources), which has a
profound effect. I used gammatones plus nonlinearity normalization in
the pitch tracker in my CASA system, but I felt like I understood it
better when I saw the way that Tolonen & Karjalainen simply whitened
the spectrum (on a suitably coarse scale) then autocorrelated a low
band (with HWR?) and a high band (with envelope extraction?) to avoid
the need for a large number of parallel channels. I think their paper
gets at a lot of the essence of many such pitch models.
Tolonen, T. and M. Karjalainen (2000). A computationally
efficient multipitch analysis model. IEEE Transactions on
Speech and Audio Processing 8(6), 708â716.