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Open AccessArticle

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Electrical and Computer Engineering, Marquette University, Milwaukee, WI 53201, USA
American Association for the Advancement of Science, Washington, DC 20005, USA
Animal Sciences, University of Connecticut, Storrs, CT 06269, USA
Oregon Zoo, Portland, OR 97221, USA
Department of Behavioural Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznań, Poland
Author to whom correspondence should be addressed.
Algorithms 2009, 2(4), 1410-1428;
Received: 1 September 2009 / Revised: 30 October 2009 / Accepted: 9 November 2009 / Published: 18 November 2009
(This article belongs to the Special Issue Algorithms for Sound Localization and Sound Classification)
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. View Full-Text
Keywords: Hidden Markov Model (HMM); Greenwood Frequency Cepstral Coefficients (GFCCs) Hidden Markov Model (HMM); Greenwood Frequency Cepstral Coefficients (GFCCs)
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MDPI and ACS Style

Ren, Y.; Johnson, M.T.; Clemins, P.J.; Darre, M.; Glaeser, S.S.; Osiejuk, T.S.; Out-Nyarko, E. A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models. Algorithms 2009, 2, 1410-1428.

AMA Style

Ren Y, Johnson MT, Clemins PJ, Darre M, Glaeser SS, Osiejuk TS, Out-Nyarko E. A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models. Algorithms. 2009; 2(4):1410-1428.

Chicago/Turabian Style

Ren, Yao; Johnson, Michael T.; Clemins, Patrick J.; Darre, Michael; Glaeser, Sharon Stuart; Osiejuk, Tomasz S.; Out-Nyarko, Ebenezer. 2009. "A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models" Algorithms 2, no. 4: 1410-1428.

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