Subject: Re: Acoustical similarity From: "Regis Rossi A. Faria" <regis@xxxxxxxx> Date: Mon, 5 Feb 2007 14:59:39 -0200 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>Hi Bruno, I have used wavelet transform to extract features (patterns/cues for expressivity content) in the past, and more recently I used PEAQ (perceptual evaluation of audio quality) techniques to measure similarity (or quality degradation) of two sounds: the original and the encoded/decoded (i.e., which has passed by a encoding/decoding process). This last one is an objective (algorithmic) audio quality evaluation method standardized by ITU under BS.1387. Kind regards, Regis James McDermott escreveu: >> 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 >