General Research Interests:

My general interest is in machine perception of music, that is to what extent is the information contained in musical signals and musical scores accessible by computer. My initial interest in this area was in pitch tracking by computer both in the frequency domain and in the time domain and in pitch perception by humans. Other problems I've studied have included the use of autocorrelation to determine musical meter, pitch perception of frequency modulated musical signals by skilled performers, harmonicity of musical instruments, limits of accuracy in performances and in pitch perception by skilled musicians, and the effects of noise on accuracy of calculations using the phase vocoder. Most recently I have been interested in musical instrument identification by computer. My initial results were an exploration of the effectiveness of speaker identification techniques using pattern recognition and a Gaussian mixture model with cepstral coefficients as features. More recently I have examined the effectiveness of a number of features in addition to cepstral coefficients, including autocorrelation coefficients, spectral centroid, spectral irregularity, and moments of the time wave, for the identification of members of the woodwind family of musical instruments. Publications are listed on my Wellesley page.

Recent Projects:

Musical instrument identification

a. Oboe/Sax identification

Calculations have been carried out using cepstral coefficients and autocorrelation coefficients as features to distinguish between sounds produced by oboes and by saxophones. These computer calculations had a higher success rate than human perception experiments on the same sounds.

b. Woodwind identification

I have examined the effectiveness of a number of features in addition to cepstral coefficients, including autocorrelation coefficients, constant Q coefficients, spectral centroid, spectral irregularity, and moments of the time wave, for the identification of members of the woodwind family of musical instruments. Most effective was spectral irregularity (84 % correct identifications) followed by cepstral coefficients and autocorrelation coefficients.

Independent Component Analysis and Non-Negative Matrix Factorization

With Paris Smaragdis we have successfully separated the notes of both trills and more complicated polyphonic musical passages using ICA and NMF. We can thus estimate the spectral profile and the temporal information of every note in these examples.

Current Project:

Classification of Vocalizations of Killer Whales

A large number of whale sounds recorded from the Captive Killer Whale Population at Marineland of Antibes, France were classified into call types using acoustic input and visual examination of spectral patterns [A. Hodgins-Davis, ``An Analysis of the Vocal Repertoire of the Captive Killer Whale Population at Marineland of Antibes, France,'' thesis, Wellesley College, 2004]. The melodic contour of these sounds was calculated using two methods of pitch tracking. [J. C. Brown et al, " Calculation of repetition rates of the vocalizations of killer whales", JASA 116, 2004]. Using dynamic time warping and a kmeans classifier, they have since been classified into 10 groups to compare with the 9 perceptual groups with roughly 90 % accuracy. [J.C. Brown and P.J.O. Miller, " Classification of Vocalizations of Killer Whales Using Dynamic Time Warping", submitted to WASPAA05] This method will be tested on other perceptually-grouped killer whale sounds, and other means of classification eliminating the tedious step of pitch tracking are being explored.