A Statistical Method for Quantifying Songbird Phonology and syntax
Wei Wu, John A. Thompson, Richard Bertram, and Frank Johnson
Songbirds are the preeminent animal model for understanding how the brain encodes and produces learned vocalizations. Here, we report a new statistical method, the Kullback-Leibler (K-L) distance, for analyzing vocal change over time. First, we use a computerized recording system to capture all song syllables produced by birds each day. Sound Analysis Pro software (Tchernichovski et al., 2000) is then used to measure the duration of each syllable as well as four spectral features: pitch, entropy, frequency modulation, and pitch goodness. Next, 2-dimensional scatter plots of each day of singing are created where syllable duration is on the x-axis and each of the spectral features is represented separately on the y-axis. Each point in the scatter plots represents one syllable and we regard these plots as random samples from a probability distribution. We then apply the standard information-theoretic quant ity K-L distance to measure dissimilarity in phonology across days of singing. A variant of this procedure can also be used to analyze differences in syllable syntax.