A procedure for an automated measurement of song similarity

Abstract

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. Copyright 2000 The Association for the Study of Animal Behaviour.