Engineering Commons^™

Acoustic Censusing Using Automatic Vocalization Classification And Identity Recognition, Kuntoro Adi, Michael T. Johnson, Tomasz S. Osiejuk

Electrical and Computer Engineering Faculty Research and Publications

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 …