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Classification of vocalizations of killer whales using dynamic time warping
Author(s) -
Judith C. Brown,
Andrea Hodgins-Davis,
Patrick J. O. Miller
Publication year - 2006
Publication title -
the journal of the acoustical society of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 187
eISSN - 1520-8524
pISSN - 0001-4966
DOI - 10.1121/1.2166949
Subject(s) - dynamic time warping , whale , fast fourier transform , computer science , speech recognition , multidimensional scaling , pattern recognition (psychology) , acoustics , artificial intelligence , algorithm , physics , machine learning , fishery , biology
A large number of killer whale sounds have recently been classified perceptually into Call Types. [A. Hodgins-Davis, thesis, Wellesley College (2004)]. The repetition rate of the pulsed component of five or more examples of each call type has been calculated using a modified form of the FFT based comb-filter method. A dissimilarity or distance matrix for these sounds was calculated using dynamic time warping to compare their melodic contours. These distances were transformed into a component space using multidimensional scaling and the resulting points were clustered with a kmeans algorithm. In grouping 57 sounds into 9 call types, a single discrepancy between the perceptual and the automated methods occurred.

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