Document Type




Format of Original

10 p.

Publication Date



Acoustical Society of America

Source Publication

Journal of the Acoustical Society of America

Source ISSN


Original Item ID

doi: 10.1121/1.3273887


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 Markovmodels (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.


Published version. Journal of the Acoustical Society of America, Vol. 127, No. 2 (February 2010): 874-883. DOI. © 2010 Acoustical Society of America. Used with permission.