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Automated classification of humpback whale (Megaptera novaeangliae) songs using hidden Markov models

Automated classification of humpback whale (Megaptera novaeangliae) songs using hidden Markov models
Automated classification of humpback whale (Megaptera novaeangliae) songs using hidden Markov models
Humpback whales songs have been widely investigated in the past few decades. This study proposes a new approach for the classification of the calls detected in the songs with the use of Hidden Markov Models (HMMs). HMMs have been used once before for such task but in an unsupervised algorithm with promising results. Here HMMs were trained and two models were employed to classify the calls into their component units and subunits. The results show that classification of humpback whale songs from one year to another is possible even with limited training. The classification is fully automated apart from the labelling of the training set and the input of the initial HMM prototype models. Two different models for the song structure are considered: one based on song units and one based on subunits. The latter model is shown to achieve better recognition results with a reduced need for updating when applied to a variety of recordings from different years and different geographic locations.
Pace, Federica
d6e4e55d-b4d5-415a-bce4-061d5682425a
Pace, Federica
d6e4e55d-b4d5-415a-bce4-061d5682425a
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba

(2013) Automated classification of humpback whale (Megaptera novaeangliae) songs using hidden Markov models. University of Southampton, Engineering and the Environment, Doctoral Thesis, 204pp.

Record type: Thesis (Doctoral)

Abstract

Humpback whales songs have been widely investigated in the past few decades. This study proposes a new approach for the classification of the calls detected in the songs with the use of Hidden Markov Models (HMMs). HMMs have been used once before for such task but in an unsupervised algorithm with promising results. Here HMMs were trained and two models were employed to classify the calls into their component units and subunits. The results show that classification of humpback whale songs from one year to another is possible even with limited training. The classification is fully automated apart from the labelling of the training set and the input of the initial HMM prototype models. Two different models for the song structure are considered: one based on song units and one based on subunits. The latter model is shown to achieve better recognition results with a reduced need for updating when applied to a variety of recordings from different years and different geographic locations.

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More information

Published date: May 2013
Organisations: University of Southampton, Signal Processing & Control Grp

Identifiers

Local EPrints ID: 364358
URI: http://eprints.soton.ac.uk/id/eprint/364358
PURE UUID: 095323b2-355a-4bf1-b118-f20e7c76429e
ORCID for Paul White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 29 May 2014 12:02
Last modified: 06 Jun 2018 13:12

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Contributors

Author: Federica Pace
Thesis advisor: Paul White ORCID iD

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