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*To*: AUDITORY@xxxxxxxxxxxxxxx*Subject*: Re: Detection of harmonics and rhythmic structure*From*: Paul Boersma <paul.boersma@xxxxxxxxxx>*Date*: Fri, 12 May 2000 02:06:30 +0200*Comments*: cc: Brian Gygi <bgygi@INDIANA.EDU>*In-reply-to*: <Pine.GSO.3.96.1000511141153.28455w-100000@kate.ucs.indiana.edu>*References*: <Pine.GSO.3.96.1000511141153.28455w-100000@kate.ucs.indiana.edu>*Reply-to*: Paul Boersma <paul.boersma@xxxxxxxxxx>*Sender*: AUDITORY Research in Auditory Perception <AUDITORY@xxxxxxxxxxxxxxx>

Brian Gygi wrote: > Does anyone know some good algorithms for determining a) the presence or > absence of harmonics in a signal (non-speech), and b) whether the signal > is discrete or rhythmic (repetitive)? I can imagine that these two > questions are related, one is in the frequency domain and one in the time > domain. I have fooled around with autocorrelations, but want to be able > to extract a number that would capture the amount of either harmonic or > rhythmic structure in a signal. The Praat program can perform a harmonicity (= harmonics-to-noise ratio) analysis, which measures the degree of periodicity of a sampled signal. You specify the minimum and maximum frequency, and the algorithm will look for repetitive wave shapes in between. It works for non-speech as well as for speech, because the thing measured is mathematical shape similarity (autocorrelation-based or crosscorrelation-based). The algorithm I use is by far the most accurate (or sensitive, if we talk about detecting noise in periodicity) of all the known algorithms, and is described in a 1993 paper, downloadable from my home page. The accuracy derives from regarding a sampled signal analytically as a sum of sinc functions, from using a Gaussian analysis window, from dividing the autocorrelation of the windowed signal by the autocorrelation of the window itself, and from taking negative lags into account so that the algorithm is accurate for repetition rates up to 80% of the Nyquist frequency. The algorithm gives a number in dB, which is equivalent to a relative periodicity power of 1/(1+10^(-dB/10)): 90 dB 0.999999999 (i.e. almost perfectly periodic) 60 dB 0.999999 30 dB 0.999 10 dB 0.91 0 dB 0.5 (i.e. as much harmonic power as noise power) -10 dB 0.09 For voicing measurements in speech, this method is used for the voiced/unvoiced decision, with the criterion usually near 0 dB. If you want to use the algorithm for analysing repetitive noises (i.e. with different phase structure in each "period"), you can take the following steps: 1. Square the signal, i.e. multiply it by itself, e.g. by using the formula "self*self" or "self^2" in Praat. Before you do that, however, make sure that the signal is band-limited to half the Nyquist frequency, i.e. one quarter of the sample rate! 2. Smooth the squared signal by convolving it with a Gaussian window (or multiply by a zero-centered Gaussian in the frequency domain, i.e. "To Spectrum", "Formula... self*exp(-(x/50)^2)", "To Sound"). 3. Do the analysis ("To Harmonicity"). The result is a HNR as a function of time. You can draw or query it. Best wishes, Paul -- Paul Boersma Institute of Phonetic Sciences, University of Amsterdam Herengracht 338, 1016CG Amsterdam, The Netherlands http://www.fon.hum.uva.nl/paul/

**References**:**Detection of harmonics and rhythmic structure***From:*Brian Gygi

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