Literature DB >> 19507925

Hidden Markov and Gaussian mixture models for automatic call classification.

Judith C Brown1, Paris Smaragdis.   

Abstract

Automatic methods of classification of animal sounds offer many advantages including speed and consistency in processing massive quantities of data. Calculations have been carried out on a set of 75 calls of Northern Resident killer whales, previously classified perceptually (human classification) into seven call types, using, hidden Markov models (HMMs) and Gaussian mixture models (GMMs). Neither of these methods has been used previously for classification of marine mammal call types. With cepstral coefficients as features both HMMs and GMMs give over 90% agreement with the perceptual classification, with the HMM over 95% for some cases.

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Year:  2009        PMID: 19507925     DOI: 10.1121/1.3124659

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  3 in total

1.  A vocal-based analytical method for goose behaviour recognition.

Authors:  Kim Arild Steen; Ole Roland Therkildsen; Henrik Karstoft; Ole Green
Journal:  Sensors (Basel)       Date:  2012-03-21       Impact factor: 3.576

2.  Automatic detection, classification, and quantification of sciaenid fish calls in an estuarine soundscape in the Southeast United States.

Authors:  Agnieszka Monczak; Yiming Ji; Jamileh Soueidan; Eric W Montie
Journal:  PLoS One       Date:  2019-01-16       Impact factor: 3.240

3.  ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning.

Authors:  Christian Bergler; Hendrik Schröter; Rachael Xi Cheng; Volker Barth; Michael Weber; Elmar Nöth; Heribert Hofer; Andreas Maier
Journal:  Sci Rep       Date:  2019-07-29       Impact factor: 4.379

  3 in total

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