Abstract：
: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 O, Nottebohm F, Ho CE, Pesaran B, Mitra PP. A procedure for an automated measurement of song similarity. Anim Behav 2000;59:1167-76] is then used to measure the duration of each syllable as well as four spectral features: pitch, entropy, frequency modulation, and pitch goodness. Next, two-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 quantity 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.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Wu W,Thompson JA,Bertram R,Johnson Fdoi
10.1016/j.jneumeth.2008.06.033subject
Has Abstractpub_date
2008-09-15 00:00:00pages
147-54issue
1eissn
0165-0270issn
1872-678Xpii
S0165-0270(08)00390-7journal_volume
174pub_type
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