[SciPy-dev] talkbox scikit

David Cournapeau cournape@gmail....
Tue Mar 3 07:07:30 CST 2009

Hi Georg

On Sat, Feb 28, 2009 at 9:50 PM, Georg Holzmann <grh@mur.at> wrote:
> Hallo David !
> I want to contribute some code to the talkbox scikit and have some
> questions.


> - First, is this scikit also for audio signal processing / audio (music)
> feature extraction, or mainly for speech only ?

No, no, music is definitely welcomed. Actually, I only do speech
because it pays my bill :) And a talkox is mainly used for music,
after all.

> - Second, I have implemented some (random) code for audio signal
> processing which IMHO would be nice to have in a scikit:
> * Implementation of a Generalized Cross Correlation (GCC) with various
> pre-whitening filters.
> (after "The Generalized Correlation Method for Estimation of Time Delay"
> by Charles Knapp and Clifford Carter, programmed with looking at the
> matlab GCC implementation by Davide Renzi)
> this function is used for robustly determine the time delay between two
> real signals
> * Equivalent Rectangular Bandwidth Filter Coefficients for biquad IIR
> Filters.
> (implemented after "An Efficient Implementation of the
> Patterson-Holdsworth Auditory Filter Bank" by Malcolm Slaney)
> * Filter coefficients for a bank of Gammatone filters.
> (implemented after "An Efficient Implementation of the
> Patterson-Holdsworth Auditory Filter Bank" by Malcolm Slaney)
> Implementation also with multiple biquad filters, to avoid numerical
> unstabilities.
> * Common filter parameters for audio biquad IIR filters (after "Cookbook
> formulae for audio EQ biquad filter coefficients",
> http://www.musicdsp.org/files/Audio-EQ-Cookbook.txt)
> * Conversion of linear IIR filter parameters to a minimum phase filter
> with the same amplitude response.
> * MFCC feature extraction (but I have seen that you already have
> implemented mfccs...)
> * I plan to implement more audio/music feature extraction methods in
> near future (chroma features, beat features, beat-synchronous features ...)

All this sounds great. I don't have much time to work on talkbox at
the moment, so I won't be able to review in detail your code. I know
more or less what I want to see in the scikits (all the above fits
it), but I don't know yet how to organize.

There are only two big requirements:
 - I do want a pure python implementation for everything (with
optional C/Cython).
 - It should be under the BSD. I hope that at least some of it will be
included in scipy at some point.

> - In which categories should I put all these ? So I propose all the
> filter parameter calculations in talkbox/fbanks/, feature extraction
> methods of course into talkbox/features/ and the generalized cross
> correlation maybe into talkbox/tools/correlations.py, or maybe in a
> seperate file ... ?

The organization is quite messy ATM, I have not thought too much about
it. The difference between features and fbanks is not clear, for
example. The good news is that I may well be the only user of talkbox
for now, so if you have a better suggestion, we can break things,

> - And last but not least, is this the right mailing list for such
> discussions ;) ? Or are there any special lists for scikits

No special scikits ML, no, I think it is the right place,


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