A machine learning approach for classifying and quantifying acoustic diversity.

ABSTRACT: 1. Assessing diversity of discretely varying behavior is a classical ethological problem. In particular, the challenge of calculating an individuals' or species' vocal repertoire size is often an important step in ecological and behavioral studies, but a reproducible and broadly applicable method for accomplishing this task is not currently available. 2. We offer a generalizable method to automate the calculation and quantification of acoustic diversity using an unsupervised random forest framework. We tested our method using natural and synthetic datasets of known repertoire sizes that exhibit standardized variation in common acoustic features as well as in recording quality. We tested two approaches to estimate acoustic diversity using the output from unsupervised random forest analyses:

SUBMITTER: Keen SC 

PROVIDER: S-EPMC8654111 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature