Bird song comparison using deep learning trained from avian perceptual judgments.

ABSTRACT: Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avi

SUBMITTER: Zandberg L 

PROVIDER: S-EPMC11333001 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature