Project

Classification of Killer Whale Sounds with GMM and HMM

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. Since this method requires the time-consuming pre-processing measurement of 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 seven call types. With cepstral coefficients as features both HMM�s and GMM�s give over 90% agreement with the perceptual classification, with the HMM over 95% for some cases.

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. Since this method requires the time-consuming pre-processing measurement of 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 seven call types. With cepstral coefficients as features both HMM�s and GMM�s give over 90% agreement with the perceptual classification, with the HMM over 95% for some cases.