TY - JOUR
T1 - A procedure for an automated measurement of song similarity
AU - Tchernichovski, Ofer
AU - Nottebohm, Fernando
AU - Ho, Ching Elizabeth
AU - Pesaran, Bijan
AU - Mitra, Partha Pratim
N1 - Funding Information:
We thank Marcelo Magnasco, Boris Shriaman, Michael Fee and Thierry Lints for their useful comments. Supported by NIMH 18343, the Mary Flagler Cary Charitable Trust and the generosity of Rommie Shapiro and the late Herbert Singer. The research presented here was described in Animal Utilization Proposal No. 93161, approved September 1998 by The Rockefeller Animal Research Ethics Board.
PY - 2000
Y1 - 2000
N2 - Assessment of vocal imitation requires a widely accepted way of describing and measuring any similarities between the song of a tutor and that of its pupil. Quantifying the similarity between two songs, however, can be difficult and fraught with subjective bias. We present a fully automated procedure that measures parametrically the similarity between songs. We tested its performance on a large database of zebra finch, Taeniopygia guttata, songs. The procedure uses an analytical framework of modern spectral analysis to characterize the acoustic structure of a song. This analysis provides a superior sound spectrogram that is then reduced to a set of simple acoustic features. Based on these features, the procedure detects similar sections between songs automatically. In addition, the procedure can be used to examine: (1) imitation accuracy across acoustic features; (2) song development; (3) the effect of brain lesions on specific song features; and (4) variability across different renditions of a song or a call produced by the same individual, across individuals and across populations. By making the procedure available we hope to promote the adoption of a standard, automated method for measuring similarity between songs or calls. (C) 2000 The Association for the Study of Animal Behaviour.
AB - Assessment of vocal imitation requires a widely accepted way of describing and measuring any similarities between the song of a tutor and that of its pupil. Quantifying the similarity between two songs, however, can be difficult and fraught with subjective bias. We present a fully automated procedure that measures parametrically the similarity between songs. We tested its performance on a large database of zebra finch, Taeniopygia guttata, songs. The procedure uses an analytical framework of modern spectral analysis to characterize the acoustic structure of a song. This analysis provides a superior sound spectrogram that is then reduced to a set of simple acoustic features. Based on these features, the procedure detects similar sections between songs automatically. In addition, the procedure can be used to examine: (1) imitation accuracy across acoustic features; (2) song development; (3) the effect of brain lesions on specific song features; and (4) variability across different renditions of a song or a call produced by the same individual, across individuals and across populations. By making the procedure available we hope to promote the adoption of a standard, automated method for measuring similarity between songs or calls. (C) 2000 The Association for the Study of Animal Behaviour.
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U2 - 10.1006/anbe.1999.1416
DO - 10.1006/anbe.1999.1416
M3 - Article
AN - SCOPUS:0033858382
SN - 0003-3472
VL - 59
SP - 1167
EP - 1176
JO - Animal Behaviour
JF - Animal Behaviour
IS - 6
ER -