Open AccessBehavioral discrimination and time-series phenotyping of birdsong performance
Author(s) -
Avishek Paul,
Helen McLendon,
Veronica Rally,
Jon T. Sakata,
Sarah C. Woolley
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008820
Subject(s) - zebra finch , motif (music) , expansive , social animal , computer science , decipher , artificial intelligence , speech recognition , time series , finch , pattern recognition (psychology) , machine learning , biology , evolutionary biology , neuroscience , bioinformatics , ecology , compressive strength , physics , materials science , acoustics , composite material
Variation in the acoustic structure of vocal signals is important to communicate social information. However, relatively little is known about the features that receivers extract to decipher relevant social information. Here, we took an expansive, bottom-up approach to delineate the feature space that could be important for processing social information in zebra finch song. Using operant techniques, we discovered that female zebra finches can consistently discriminate brief song phrases (“motifs”) from different social contexts. We then applied machine learning algorithms to classify motifs based on thousands of time-series features and to uncover acoustic features for motif discrimination. In addition to highlighting classic acoustic features, the resulting algorithm revealed novel features for song discrimination, for example, measures of time irreversibility (i.e., the degree to which the statistical properties of the actual and time-reversed signal differ). Moreover, the algorithm accurately predicted female performance on individual motif exemplars. These data underscore and expand the promise of broad time-series phenotyping to acoustic analyses and social decision-making.