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Re: Acoustical similarity
there may be something of interest to you in Villa Pulkki's approach at
HUT.Fi. he algorithmically separates source and reflected sound to good
>>> "Bruno L. Giordano" <bruno.giordano@xxxxxxxxxxxxxxx> 05/02/2007
I use features for the quantification of the acoustical correlates of
However, the very process of the definition of the features requires
making guesses (i.e., assumptions) about what is perceptually
While this problem might not be incredibly pressing for the researcher
working on simple, synthetic stimuli, it can become painful when
perception of complex everyday sounds is of interest. Indeed, given the
informational richness of these latter, it is possible that the
researcher, in the features-definition process, does not capture all of
what is used by a listener.
Therefore the idea of general metrics.
James McDermott wrote:
>> From: "Bruno L. Giordano"
>> I am looking for "general" metrics of the acoustical (not
>> similarity between mono signals independent of a features
>> stage (e.g., peak level, harmonicity etc.).
>> Ideally, this metric would operate on a low-level representation of
>> signal (ideally the waveform).
> Hi Bruno,
> I am doing work which involves measuring similarity for machine
> learning applications. One standard method (eg in evolutionary
> computation) is to take a mean square error over the magnitude or
> power spectrum: ie for two signals x and y of length N, window them
> and take the DFT of each window and then take the magnitude of each
> bin, to produce two sequences of spectra, X_i and Y_i: the distance
> d(x, y) = sum_i (sum_n (X_i[j] - Y_i[j]) ^2)
> You can indeed define a purely time-domain distance measure:
> d(x, y) = sum_n (x[n] - y[n]) / N
> but it seems to be pretty useless: eg if we construct y by
> phase-inverting x, we get a very large distance between them, even
> though they sound exactly the same.
> As you know, in other applications (such as automatic
> the extraction of features is more common.
> I'd be interested to hear more about your application and why you
> don't want to extract features?
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