Document Type

Article

Language

eng

Format of Original

19 p.

Publication Date

11-2009

Publisher

MDPI

Source Publication

Algorithms

Source ISSN

1999-4893

Original Item ID

doi: 10.3390/a2041410

Abstract

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks.

Comments

Published version. Algorithms, Vol. 2, No. 4 (November 2009): 1410-1428. DOI. © MDPI 2009. Published under Creative Commons Attribution License 3.0.

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