Automated analysis of song structure in complex birdsongs. (February 2016) - Große Ruse, Mareile; Hasselquist, Dennis; Hansson, Bengt; Tarka, Maja; Sandsten, Maria
Abstract : Understanding communication and signalling has long been strived for in studies of animal behaviour. Many songbirds have a variable and complex song, closely connected to territory defence and reproductive success. However, the quantification of such variable song is challenging. In this paper, we present a novel, automated method for detection and classification of syllables in birdsong. The method provides a tool for pairwise comparison of syllables with the aim of grouping them in terms of their similarity. This allows analyses such as (1) determining repertoire size within an individual, (2) comparing song similarity between individuals within as well as between populations of a species and (3) comparing songs of different species (e.g. for species recognition). Our method is based on a particular feature representation of song units (syllables) which ensures invariance to shifts in time, frequency and amplitude. Using a single song from a great reed warbler, Acrocephalus arundinaceus, recorded in the wild, the proposed algorithm is evaluated by means of comparison to manual auditory and visual (spectrogram) song investigation by a human expert and to standard song analysis methods. Our birdsong analysis approach conforms well to manual classification and, moreover, outperforms the hitherto widely used methods based on mel-frequency cepstral coefficients and spectrogram cross-correlation. Thus, our algorithm is a methodological step forward for analyses of song (syllable) repertoires of birds singing with high complexity. Highlights: Automated method for detection/classification of complex birdsong syllables. Application/evaluation to song of great reed warbler. Method is suitable for recordings in wild, with much background noise. For e.g. determining syllable repertoire size, song matching, song comparisons. Method conforms well to human expert classification, outperforms standard approaches.