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Algorithms 2009, 2(4), 1410-1428; doi:10.3390/a2041410
Article

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

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Received: 1 September 2009; in revised form: 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)
Download PDF [239 KB, uploaded 19 November 2009]
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.
Keywords: Hidden Markov Model (HMM); Greenwood Frequency Cepstral Coefficients (GFCCs) Hidden Markov Model (HMM); Greenwood Frequency Cepstral Coefficients (GFCCs)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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