Abstract:
Finding and censusing birds and other animals via listening can pose problems because of inaccessibility of habitats, rarity or shyness of animals, or subjectivity of observers. A new collaborative project seeks to evaluate algorithms adapted from human speech recognition to establish a basis for automating the identification of animal vocalizations and recording their occurrence. The algorithms include dynamic time warping and hybrid hidden Markov models incorporating features of artificial neural networks. Probably no single method will work for all species. More than one method maybe useful together, in multiple stages. A database of high-quality, annotated digital field recordings is being collected to supply training and test data on known species and, when possible, known individuals. Both low-noise and realistic ambient noise situations are important. Field data are supplemented with recordings from laboratory settings. Red-cockaded woodpecker, other vocal yet threatened species, and species related to them, such as other Picoides woodpeckers, are being studied. Preliminary results are presented. [Research supported by USACERL.]