Subject: Re: Acoustical similarity From: James McDermott <jamesmichaelmcdermott@xxxxxxxx> Date: Mon, 5 Feb 2007 10:17:25 +0000 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>> From: "Bruno L. Giordano" > > I am looking for "general" metrics of the acoustical (not perceived) > similarity between mono signals independent of a features extraction > stage (e.g., peak level, harmonicity etc.). > > Ideally, this metric would operate on a low-level representation of the > 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 is then 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 classification), 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? James -- James McDermott PhD candidate in Music Technology CSG026, Dept. Computer Science and Information Systems, University of Limerick, Ireland. www.skynet.ie/~jmmcd