Unsupervised bird song syllable classification using evolving neural networks.

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.

[1]  S. Furui,et al.  Cepstral analysis technique for automatic speaker verification , 1981 .

[2]  B. John Oommen.  NORTH-HOLLAND String Alignment With Substitution , Insertion , Deletion , 2022 .

[3]  Erkki Oja,et al.  The evolving tree-analysis and applications , 2006, IEEE Transactions on Neural Networks.

[4]  H. Slabbekoorn,et al.  Cities Change the Songs of Birds , 2006, Current Biology.

[5]  Judith C Brown,et al.  Classification of vocalizations of killer whales using dynamic time warping. , 2006, The Journal of the Acoustical Society of America.

[6]  Karen I. Hallberg,et al.  Cultural Evolution of Puget Sound White‐Crowned Sparrow Song Dialects , 2004 .

[7]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[8]  C. E. Ho,et al.  A procedure for an automated measurement of song similarity , 2000, Animal Behaviour.

[9]  B. Hatchwell,et al.  Individuality in the contact calls of cooperatively breeding long-tailed tits (Aegithalos caudatus) , 2005 .

[10]  Gaël Richard,et al.  Musical instrument recognition by pairwise classification strategies , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  P. Warren.  GEOGRAPHIC VARIATION AND DIALECTS IN SONGS OF THE BRONZED COWBIRD (MOLOTHRUS AENEUS) , 2002 .

[12]  P. Marler,et al.  Culturally Transmitted Patterns of Vocal Behavior in Sparrows , 1964, Science.

[13]  Mark D Skowronski,et al.  Acoustic detection and classification of Microchiroptera using machine learning: lessons learned from automatic speech recognition. , 2006, The Journal of the Acoustical Society of America.

[14]  A. Baker,et al.  A Population Memetics Approach to Cultural Evolution in Chaffinch Song: Meme Diversity Within Populations , 1993, The American Naturalist.

[15]  M.T. Johnson,et al.  Automatic Song-Type Classification and Speaker Identification of Norwegian Ortolan Bunting (Emberiza Hortulana) Vocalizations , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[16]  M. Leek,et al.  Auditory temporal resolution in birds: discrimination of harmonic complexes. , 2002, The Journal of the Acoustical Society of America.

[17]  L. Baptista.  Geographic Variation in Song and Dialects of the Puget Sound White-Crowned Sparrow , 1977 .

[18]  D Margoliash,et al.  Template-based automatic recognition of birdsong syllables from continuous recordings. , 1996, The Journal of the Acoustical Society of America.

[19]  Grzegorz Kondrak,et al.  Phonetic Alignment and Similarity , 2003, Comput. Humanit..

[20]  J A Kogan,et al.  Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study. , 1998, The Journal of the Acoustical Society of America.

[21]  Panu Somervuo,et al.  Self-Organizing Maps and Learning Vector Quantization for Feature Sequences , 1999, Neural Processing Letters.

[22]  Erkki Oja,et al.  The Evolving Tree—A Novel Self-Organizing Network for Data Analysis , 2004, Neural Processing Letters.