A procedure for an automated measurement of song similarity

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 diffic ult and fraught with subjective bias. We present a fully automated procedur e 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 sup erior sound spectrogram that is then reduced to a set of simple acoustic fe atures. Based on these features, the procedure detects similar sections bet ween songs automatically. In addition, the procedure can be used to examine : (1) imitation accuracy across acoustic features; (2) song de development; (3) the effect of brain lesions on specific song features; and (4) variabi lity across different renditions of a song or a call produced by the same i ndividual, across individuals and across populations. By making the procedu re available we hope to promote the adoption of a standard, automated metho d for measuring similarity between songs or calls. (C) 2000 The Association for the Study of Animal Behaviour.