Automatic classification of vocalizations with Gaussian Mixture Models and Hidden Markov Models

Abstract

The automatic classification of marine mammal sounds is very attractive as a means of assessing massive quantities of recorded data, freeing humans and offering rigorous and consistent output. Calculations on a set of vocalizations of Northern Resident killer whales using Dynamic Time Warping have been reported recently. [Brown, J. C., and P.J.O. Miller, ''Automatic classification of killer whale vocalizations using dynamic time warping,'' J. Acoust. Soc. Am. 122, 1201‐1207, (2007).] Since this method requires the time‐consuming pre‐processing measurement of the frequency contours, we have explored the use of Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM). These methods can be applied directly to time‐frequency decompositions of the recorded signals. Calculations have been made on a set of 75 calls previously classified perceptually into 7 call types. Preliminary results give an agreement of roughly 85% agreement with the perceptual classification for the GMM and over 90% for an HMM.