title: Hidden Markov and Gaussian mixture models for automatic call classification.
abstract: Automatic methods of classification of animal sounds offer many advantages including speed and consistency in processing massive quantities of data. Calculations have been carried out on a set of 75 calls of Northern Resident killer whales, previously classified perceptually (human classification) into seven call types, using, hidden Markov models (HMMs) and Gaussian mixture models (GMMs). Neither of these methods has been used previously for classification of marine mammal call types. With cepstral coefficients as features both HMMs and GMMs give over 90% agreement with the perceptual classification, with the HMM over 95% for some cases.
authors.affiliation: Physics Department, Wellesley College, Wellesley, Massachusetts 02481, USA. brown@media.mit.edu
authors.forename: Judith C
authors.initials: JC
authors.lastname: Brown
authors.forename: Paris
authors.initials: P
authors.lastname: Smaragdis
mesh_terms: D000818:Animals; D005583:Fourier Analysis; D008390:Markov Chains; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D014828:Vocalization, Animal; D050685:Whale, Killer