Quantitative tools for examining the vocalizations of juvenile songbirds.
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.
Laboratory of Biological Modeling, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA.
Cameron D
CD
Wellock
George N
GN
Reeke
D000375:Aging; D000465:Algorithms; D000818:Animals; D007858:Learning / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D020308:Songbirds / Q000502:physiology*; D013018:Sound Spectrography / Q000379:methods; D014828:Vocalization, Animal / Q000502:physiology*