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Automated analysis of song structure in complex birdsongs

  • Mareile Große Ruse
  • Dennis Hasselquist
  • Bengt Hansson
  • Maja Tarka
  • Maria Sandsten
Publishing year: 2016-02-01
Language: English
Pages: 39-51
Publication/Series: Animal Behaviour
Volume: 112
Document type: Journal article (comment)
Publisher: Elsevier Ltd

Abstract english

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.


  • Zoology
  • Ambiguity spectrum
  • Automated song recognition
  • Birdsong
  • Clustering
  • Great reed warbler
  • Multitaper
  • Song analysis
  • Syllable detection


  • ISSN: 0003-3472
Dennis Hasselquist
E-mail: dennis [dot] hasselquist [at] biol [dot] lu [dot] se



+46 46 222 37 08



Research group


Doctoral students and postdocs

Research fellows


Jacob Roved

PhD Students, main supervisor

PhD Students, assistant supervisor


Interview about my research in the Swedish podcast "Forskarn & jag"