Classification of humpback whale vocalizations using a self-organizing neural network

Eduardo Mercado; Anthony Kuh

Classification of humpback whale vocalizations using a self-organizing neural network

Eduardo Mercado
, Anthony Kuh

Psychology

University at Buffalo

University of Hawai'i at Mānoa

Research output: Contribution to conference › Paper › peer-review

24 Scopus citations

Abstract

This paper describes a system for classifying vocalizations of humpback whales based on a source-filter model of sound production combined with a self-organizing feature map. Individual vocalizations were characterized in terms of their pitch, duration, noisiness, and format structure using a combination of linear prediction, cepstral processing, and manual measurements. Vectors characterizing a sample of 242 sounds were then classified using a self-organizing feature map. The neural network partitioned vocalizations into categories that matched perceptually based classifications.

Original language	English
Pages	1584-1589
Number of pages	6
State	Published - 1998
Event	Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA Duration: May 4 1998 → May 9 1998

Conference

Conference	Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
City	Anchorage, AK, USA
Period	05/4/98 → 05/9/98

Cite this

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title = "Classification of humpback whale vocalizations using a self-organizing neural network",

abstract = "This paper describes a system for classifying vocalizations of humpback whales based on a source-filter model of sound production combined with a self-organizing feature map. Individual vocalizations were characterized in terms of their pitch, duration, noisiness, and format structure using a combination of linear prediction, cepstral processing, and manual measurements. Vectors characterizing a sample of 242 sounds were then classified using a self-organizing feature map. The neural network partitioned vocalizations into categories that matched perceptually based classifications.",

author = "Eduardo Mercado and Anthony Kuh",

year = "1998",

language = "English",

pages = "1584--1589",

note = "Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) ; Conference date: 04-05-1998 Through 09-05-1998",

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TY - CONF

T1 - Classification of humpback whale vocalizations using a self-organizing neural network

AU - Mercado, Eduardo

AU - Kuh, Anthony

PY - 1998

Y1 - 1998

N2 - This paper describes a system for classifying vocalizations of humpback whales based on a source-filter model of sound production combined with a self-organizing feature map. Individual vocalizations were characterized in terms of their pitch, duration, noisiness, and format structure using a combination of linear prediction, cepstral processing, and manual measurements. Vectors characterizing a sample of 242 sounds were then classified using a self-organizing feature map. The neural network partitioned vocalizations into categories that matched perceptually based classifications.

AB - This paper describes a system for classifying vocalizations of humpback whales based on a source-filter model of sound production combined with a self-organizing feature map. Individual vocalizations were characterized in terms of their pitch, duration, noisiness, and format structure using a combination of linear prediction, cepstral processing, and manual measurements. Vectors characterizing a sample of 242 sounds were then classified using a self-organizing feature map. The neural network partitioned vocalizations into categories that matched perceptually based classifications.

UR - https://www.scopus.com/pages/publications/0031624307

M3 - Paper

SP - 1584

EP - 1589

T2 - Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)

Y2 - 4 May 1998 through 9 May 1998

ER -

Classification of humpback whale vocalizations using a self-organizing neural network

Abstract

Conference

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