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From: pankaj@bass.bu.edu (Pankaj Tyagi)
Newsgroups: comp.dsp
Subject: Re: Autocorrelation Pitch Tracker
Message-ID: <78395@bu.edu.bu.edu>
Date: 4 Apr 91 20:53:10 GMT
References: <17823@sdcc6.ucsd.edu>
Sender: news@bu.edu.bu.edu
Organization: Boston University
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In article <17823@sdcc6.ucsd.edu> gbell@sdcc13.ucsd.edu (Greg Bell) writes:
>Does anybody out there have experience implementing the
>autocorrelation method of pitch detection?  I have the algorithm in
>front of me, and some code that works for a large sample, but am
>having problems getting the thing to reliably work on shorter
>samples of data (30ms, where the signal only repeats only twice).
>
>Thanks!
>
>-- 
>-----------------------------------------------------------------------------
>  Who:  Greg Bell                            Address:  gbell@ucsd.edu
> What:  EE hobbyist and major                  Where:  UC San Diego
>-----------------------------------------------------------------------------

	The autocorrelation algorithm is not suited for pitch-detection/
spectral estimation for short range data. Try covariance method if you are
sure that the data is bound to give a stable system(stability in covariance
method is not guaranteed). The reason why autocorrelation method fails  for
short range data is due to the fact that it has edge effects of the implict 
window it uses in the algoritm formulation.

	Well if you are dealing with narrowband signals, and have short data
range then the best bet is probably the Burg's method. It is very much
like the Levinson's recursion for autocorrelation, only faster and it
does not suffer from the 'edge-effect' that autocorrelation does. 
	You can find useful information on the Burg's method in
"Advanced Digital Signal Processing" Edited by Oppenheim and Lim , under
chapter 2.

Pankaj Tyagi

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p.s. I had a C code for Burg's algorithm and I can't trace it now,
	I'll email it to you if I can find it.  pt
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