title: Semi-automatic classification of bird vocalizations using spectral peak tracks.
abstract: Automatic off-line classification and recognition of bird vocalizations has been a subject of interest to ornithologists and pattern detection researchers for many years. Several new applications, including bird vocalization classification for aircraft bird strike avoidance, will require real time classification in the presence of noise and other disturbances. The vocalizations of many common bird species can be represented using a sum-of-sinusoids model. An experiment using computer software to perform peak tracking of spectral analysis data demonstrates the usefulness of the sum-of-sinusoids model for rapid automatic recognition of isolated bird syllables. The technique derives a set of spectral features by time-variant analysis of the recorded bird vocalizations, then performs a calculation of the degree to which the derived parameters match a set of stored templates that were determined from a set of reference bird vocalizations. The results of this relatively simple technique are favorable for both clean and noisy recordings.
authors.affiliation: Department of Electrical and Computer Engineering, Montana State University, Bozeman, Montana 59717-3780, USA. chen@montana.edu
authors.forename: Zhixin
authors.initials: Z
authors.lastname: Chen
authors.forename: Robert C
authors.initials: RC
authors.lastname: Maher
mesh_terms: D000818:Animals; D001717:Birds / Q000502:physiology*; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000295:instrumentation*; D008954:Models, Biological; D009622:Noise; D013018:Sound Spectrography / Q000379:methods / Q000662:veterinary*; D014828:Vocalization, Animal / Q000145:classification* / Q000502:physiology*