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Birds produce multiple types of vocalizations that, together, constitute avocal repertoire. For some species, the repertoire size is of importancebecause it informs us about their brain capacity, territory size or socialbehaviour. Estimating the repertoire size is challenging because it requireslarge amounts of data which can be difficult to obtain and analyse. From birdsvocalizations recordings, songs are extracted and segmented as sequences ofsyllables before being clustered. Segmenting songs in such a way can be doneeither by simple enumeration, where one counts unique vocalization types untilthere are no new types detected, or by specific algorithms permittingreproducible studies. In this paper, we present a specific automatic method tocompute a syllable distance measure that allows an unsupervised classificationof bird song syllables. The results obtained from the segmenting of the birdsongs are evaluated using the Silhouette metric score.

Estimating the Repertoire Size in Birds using Unsupervised Clustering techniques