Hay, any peeps around here that use YIN?  or pYIN?
Some of you who hang around the DSP Stack Exchange might know that I am 
unimpressed with YIN, namely that I don't think there is anything novel about 
it (w.r.t. Average Squared Difference Function, ASDF) other than this
so-called "Cumulative Mean Normalized Difference Function"(CMNDF) which seems 
to have the only purpose to prevent choosing the lag of 0 as the best-fit lag.  
Big Deeeel.  There are other ways to do that, and otherwise the CMNDF just 
fucks up the ASDF result, at least a little, at
the lags around the period length.  And ASDF is still the measure of best fit.  
Here is where I complain a little about YIN:
> 
>https://dsp.stackexchange.com/questions/51823/yin-pitch-estimation-algoritm-simplified-explanation/51842#51842
> 

        Here is a copy of the original YIN:

        > [Cheveigne A, Kawahara H. - *YIN, a fundamental frequency estimator 
for speech and music*](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf )

         

        and the new, improved probabilistic YIN:

        > [Mauch M, Dixon S. - *PYIN: A fundamental frequency estimator using 
probabilistic threshold 
distributions*](http://matthiasmauch.de/_pdf/mauch_pyin_2014.pdf )

Now, while I don't want to use YIN to find pitch candidates (I think I do a 
better job of it with just the ASDF), I am curious about pYIN in what exactly 
they do with the pitch candidates.  I understand Hidden Markov Models, or at 
least I used to, but I do not know what Mauch and Dixon do to
actually pick the final candidate.  Has anyone else slogged through this enough 
to understand what hey are doing?  How do they connect a candidate from the 
previous frame to a candidate of the current frame?, and then, how does pYIN 
score each candidate and choose the candidate that will
be output as the pitch?
If anyone worked on this, please lemme know.

--



r b-j                         r...@audioimagination.com



"Imagination is more important than knowledge."

 
 
 
 
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