Acoustic censusing using automatic vocalization classification and identity recognition

Kuntoro Adi, Michael T. Johnson, Tomasz S. Osiejuk

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markov models (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal species, and this combined with the widespread success of speaker identification and verification methods for human speech suggests that robust automatic identification of individuals from their vocalizations is attainable. Only a few studies, however, have yet attempted to use individual acoustic distinctiveness to directly assess population density and structure. The approach introduced here offers a direct mechanism for using individual vocal variability to create simpler and more accurate population assessment tools in vocally active species.

Original languageEnglish
Pages (from-to)874-883
Number of pages10
JournalJournal of the Acoustical Society of America
Issue number2
StatePublished - 2010

Bibliographical note

Funding Information:
The authors would like to thank the National Science Foundation (Grant No. IIS-0326395 “The Dr. Dolittle Project”) for supporting this work.

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics


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