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Automatic detection and classification of odontocete whistles

Automatic detection and classification of odontocete whistles
Automatic detection and classification of odontocete whistles
Methods for the fully automatic detection and species classification of odontocete whistles are described. The detector applies a number of noise cancellation techniques to a spectrogram of sound data and then searches for connected regions of data which rise above a pre-determined threshold. When tested on a dataset of recordings which had been carefully annotated by a human operator, the detector was able to detect (recall) 79.6% of human identified sounds that had a signal-to-noise ratio above 10?dB, with 88% of the detections being valid. A significant problem with automatic detectors is that they tend to partially detect whistles or break whistles into several parts. A classifier has been developed specifically to work with fragmented whistle detections. By accumulating statistics over many whistle fragments, correct classification rates of over 94% have been achieved for four species. The success rate is, however, heavily dependent on the number of species included in the classifier mix, with the mean correct classification rate dropping to 58.5% when 12 species were included
acoustic signal detection, acoustic signal processing, bioacoustics, biocommunications, signal classification, signal denoising
0001-4966
2427-2437
Gillespie, Douglas
bd154eb4-02b9-485f-8b14-331398eec520
Caillat, Majolaine
a0ff9949-2b4a-4134-9aba-cc7682261874
Gordon, Jonathan
ebaf0f57-b200-4d62-a7d5-dfcbd596d00f
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Gillespie, Douglas
bd154eb4-02b9-485f-8b14-331398eec520
Caillat, Majolaine
a0ff9949-2b4a-4134-9aba-cc7682261874
Gordon, Jonathan
ebaf0f57-b200-4d62-a7d5-dfcbd596d00f
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba

Gillespie, Douglas, Caillat, Majolaine, Gordon, Jonathan and White, P.R. (2013) Automatic detection and classification of odontocete whistles. [in special issue: Methods for Marine Mammals Passive Acoustics] Journal of the Acoustical Society of America, 134 (3), 2427-2437. (doi:10.1121/1.4816555).

Record type: Article

Abstract

Methods for the fully automatic detection and species classification of odontocete whistles are described. The detector applies a number of noise cancellation techniques to a spectrogram of sound data and then searches for connected regions of data which rise above a pre-determined threshold. When tested on a dataset of recordings which had been carefully annotated by a human operator, the detector was able to detect (recall) 79.6% of human identified sounds that had a signal-to-noise ratio above 10?dB, with 88% of the detections being valid. A significant problem with automatic detectors is that they tend to partially detect whistles or break whistles into several parts. A classifier has been developed specifically to work with fragmented whistle detections. By accumulating statistics over many whistle fragments, correct classification rates of over 94% have been achieved for four species. The success rate is, however, heavily dependent on the number of species included in the classifier mix, with the mean correct classification rate dropping to 58.5% when 12 species were included

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Published date: September 2013
Keywords: acoustic signal detection, acoustic signal processing, bioacoustics, biocommunications, signal classification, signal denoising
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 357466
URI: http://eprints.soton.ac.uk/id/eprint/357466
ISSN: 0001-4966
PURE UUID: db87b909-b4f3-415f-9f35-5e94207e4f61
ORCID for P.R. White: ORCID iD orcid.org/0000-0002-4787-8713

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Date deposited: 07 Oct 2013 14:17
Last modified: 15 Mar 2024 02:41

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Contributors

Author: Douglas Gillespie
Author: Majolaine Caillat
Author: Jonathan Gordon
Author: P.R. White ORCID iD

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