======= On Monday 03 July 2006 11:07, Denis Sbragion wrote: =======
Hello Andrew,
...
See the Mourjopoulos
papers for further details.
Bye,
-- Denis Sbragion InfoTecna Tel: +39 0362 805396, Fax: +39 0362 805404 URL: http://www.infotecna.it ====================================================================================== Denis, I have not found free description of the method defined in this article: P.Hatziantoniou,J.Mourjopoulos,"Generalized Fractional-Octave Smoothing of Audio and Acoustic Responses", Journal of the Audio Engineering Society, Vol. 48, No. 4, pp. 259 - 280, April 2000. At any case, I have tried very simple smoothing of FFT-output, when y(n) is an arithmetic mean of x(n) and few other input signals before x(n). This "few other" is proportional to n. Result of "1/6 octave smoothing" is here: http://gaydenko.com/mix/simpleSmoothing.png "For eyes", the result is absolutely appropriate for my audio-DIY-ing purposes. Probably, shown below short python fragment is better rather my ugly English. The fragment is applied just after FFT-ing (i.e., before converting to db). Andrew =============================== import Numeric def smooth( x, # real octaveFraction = 1.01 # for "1/3 octave smoothing" # this par is supposed to be a pow(2.0, 1.0/3.0) ): y = [] for n in range(len(x)): sum = x[n] meanLength = int( (octaveFraction - 1.0) * n / 2.0 ) if meanLength > 0: for k in range(meanLength): idx = n - k if(idx < 0): idx = 0 # to avoid edge effect sum += x[idx] y.append(sum / (meanLength + 1)) return Numeric.array(y)Received on Thu Jul 6 04:15:04 2006
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