A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

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, a...

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Bibliographic Details
Main Authors: Ebenezer Out-Nyarko, Sharon Stuart Glaeser, Michael Darre, Patrick J. Clemins, Michael T. Johnson, Yao Ren, Tomasz S. Osiejuk
Format: Article
Language:English
Published: MDPI AG 2009-11-01
Series:Algorithms
Subjects:
Hidden Markov Model (HMM)
Greenwood Frequency Cepstral Coefficients (GFCCs)
Online Access:http://www.mdpi.com/1999-4893/2/4/1410/

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http://www.mdpi.com/1999-4893/2/4/1410/

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