Unsupervised bird song syllable classification using evolving neural networks

Abstract

Evolution of bird vocalizations is subjected to selection pressure related to their functions. Passerine bird songs are also under a neutral model of evolution because of the learning process supporting their transmission; thus they contain signals of individual, population, and species relationships. In order to retrieve this information, large amounts of data need to be processed. From vocalization recordings, songs are extracted and encoded as sequences of syllables before being compared. Encoding songs in such a way can be done either by ear and spectrogram visual analysis or by specific algorithms permitting reproducible studies. Here, a specific automatic method is presented to compute a syllable distance measure allowing an unsupervised classification of song syllables. Results obtained from the encoding of White-crowned Sparrow (Zonotrichia leucophrys pugetensis) songs are compared to human-based analysis.