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Algorithms
Special Issues
Algorithms for Sound Localization and Sound Classification
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Algorithms for Sound Localization and Sound Classification

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 March 2010) | Viewed by 22284

Special Issue Editor

Dr. David Chesmore

E-Mail Website
Guest Editor
Department of Electronics, University of York, Heslington, York YO10 5DD, UK
Interests: application of electronics and computing technology to biology, ecology and entomology, particularly in the areas of automated species identification and biodiversity informatics

Keywords

  • computational bioacoustics
  • sound classification
  • automated identification
  • pattern recognition
  • acoustic source separation

Published Papers (2 papers)

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239 KiB  
Article
A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models
by Yao Ren, Michael T. Johnson, Patrick J. Clemins, Michael Darre, Sharon Stuart Glaeser, Tomasz S. Osiejuk and Ebenezer Out-Nyarko
Algorithms 2009, 2(4), 1410-1428; https://doi.org/10.3390/a2041410 - 18 Nov 2009
Cited by 39 | Viewed by 11946
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Algorithms for Sound Localization and Sound Classification)
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488 KiB  
Article
Classification of Sperm Whale Clicks (Physeter Macrocephalus) with Gaussian-Kernel-Based Networks
by Mike Van der Schaar, Eric Delory and Michel André
Algorithms 2009, 2(3), 1232-1247; https://doi.org/10.3390/a2031232 - 22 Sep 2009
Cited by 6 | Viewed by 9932
Abstract
With the aim of classifying sperm whales, this report compares two methods that can use Gaussian functions, a radial basis function network, and support vector machines which were trained with two different approaches known as C-SVM and ν-SVM. The methods were [...] Read more.
With the aim of classifying sperm whales, this report compares two methods that can use Gaussian functions, a radial basis function network, and support vector machines which were trained with two different approaches known as C-SVM and ν-SVM. The methods were tested on data recordings from seven different male sperm whales, six containing single click trains and the seventh containing a complete dive. Both types of classifiers could distinguish between the clicks of the seven different whales, but the SVM seemed to have better generalisation towards unknown data, at the cost of needing more information and slower performance. Full article
(This article belongs to the Special Issue Algorithms for Sound Localization and Sound Classification)
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