Automatic frequency feature extraction for bird species delimitation

Abstract

Zoologists have long studied species distinctions, but until recently a quantitative system which could be applied to all birds which satisfies rigor and repeatability was absent from the zoology literature. A system which uses morphology, acoustic and plumage evidence to review species status of bird populations was presented by Tobias et al. The acoustic evidence in that work was extracted using manual inspection of spectrograms. The current work seeks to automate this process. Signal processing techniques are employed in this paper to automate the extraction of the acoustic features: maximum, minimum and peak frequency, and bandwidth. YIN-bird, a pitch detection algorithm optimized for birds, and sine-track method, successfully applied to bird species recognition previously, are the automatic methods employed. The performance of automatic methods is compared to the manual method currently used by zoologists. Both methods are well suited to this task, and demonstrate the strong potential to begin to automate the task of acoustic comparison of bird species.