Quantitative tools for examining the vocalizations of juvenile songbirds

Authors:
Cameron D. Wellock;George N. Reeke
Affiliations:
Laboratory of Biological Modeling, The Rockefeller University, New York, NY;Laboratory of Biological Modeling, The Rockefeller University, New York, NY
Venue:
Computational Intelligence and Neuroscience
Year:
2012

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Abstract

The singing of juvenile songbirds is highly variable and not well stereotyped, a feature that makes it difficult to analyze with existing computational techniques. We present here a method suitable for analyzing such vocalizations, windowed spectral pattern recognition (WSPR). Rather than performing pairwise sample comparisons, WSPR measures the typicality of a sample against a large sample set. We also illustrate how WSPR can be used to perform a variety of tasks, such as sample classification, song ontogeny measurement, and song variability measurement. Finally, we present a novel measure, based on WSPR, for quantifying the apparent complexity of a bird's singing.