Automated
analysis of song structure in complex birdsongs
In this paper, I want present a novel, automated method
for detection and classification of syllables in birdsong. The method provides
a tool for pairwise comparison of syllables with the aim of grouping them in
terms of their similarity. Our method is based on a particular feature
representation of song units (syllables) which ensures invariance to shifts in
time, frequency and amplitude. Our birdsong analysis approach conforms well to
manual classification and, moreover, outperforms the hitherto widely used
methods based on mel-frequency cepstral coefficients and spectrogram
cross-correlation. Thus, our algorithm is a methodological step forward for
analyses of song (syllable) repertoires of birds singing with high complexity.
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