Using self-organizing maps to classify humpback whale song units and quantify their similarity

Allen, Jenny A., Murray, Anita, Noad, Michael J., Dunlop, Rebecca A. and Garland, Ellen C. (2017) Using self-organizing maps to classify humpback whale song units and quantify their similarity. Journal of the Acoustical Society of America, 142 4: 1943-1952. doi:10.1121/1.4982040

Author Allen, Jenny A.
Murray, Anita
Noad, Michael J.
Dunlop, Rebecca A.
Garland, Ellen C.
Title Using self-organizing maps to classify humpback whale song units and quantify their similarity
Journal name Journal of the Acoustical Society of America   Check publisher's open access policy
ISSN 0001-4966
Publication date 2017-10-01
Year available 2017
Sub-type Article (original research)
DOI 10.1121/1.4982040
Open Access Status Not yet assessed
Volume 142
Issue 4
Start page 1943
End page 1952
Total pages 10
Place of publication Melville, NY, United States
Publisher A I P Publishing
Language eng
Abstract Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002 to 2014, a subset of vocal signals was acoustically measured and then classified using a Self-Organizing Map (SOM). The SOM created (1) an acoustic dictionary of units representing the song's repertoire, and (2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets and can be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification. (C) 2017 Acoustical Society of America.
Keyword Bottle-Nosed Dolphins
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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Created: Sun, 05 Nov 2017, 09:04:29 EST