Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny.

ABSTRACT: Birdsong is a longstanding model system for studying evolution and biodiversity. Here, we collected and analyzed high quality song recordings from seven species in the family Estrildidae. We measured the acoustic features of syllables and then used dimensionality reduction and machine learning classifiers to identify features that accurately assigned syllables to species. Species differences were captured by the first 3 principal components, corresponding to basic frequency, power distribution, and spectrotemporal features. We then identified the measured features underlying classification accuracy. We found that fundamental frequency, mean frequency, spectral flatness, and syllable duration were the most informative features for species identification. Next, we tested whether specific aco

SUBMITTER: Rivera M 

PROVIDER: S-EPMC10151348 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature