We describe a technique for the automated analysis of continuous, unsegmented animal vocalizations. The algorithm is based on the comparison of templates with unknown signals using a dynamic time-warping (DTW) algorithm. It directly compares signal spectrograms, and identifies constituents and constituent boundaries, thus permitting the identification of a broad range of signals and signal components. When applied to the vocalizations of an indigo bunting (Passerina cyanea) and a zebra finch (Taeniopygia guttata), the recognizer identifies stereotyped songs and calls with greater than 97% accuracy. We demonstrate how DTW can also be used to assess the variation present in an animal's repertoire. This technique has general applicability to analysis of a variety of animal vocalizations and can dramatically decrease the amount of time spent on manual identification of vocalizations. The sound spectrograph is the principle tool used in analysis of bioacoustic signals. Based on visual insp..
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