Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation.

ABSTRACT:

Background

Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations. However, in practice, recording sites also have site-specific variations and unique background audio that need to be removed, warranting the need for site-specific data.

Methods

Here, we test the potential of training DSSS models on site-specific bird vocalizations and background audio. We used a semiautomated workflow using deep supervised classificat

SUBMITTER: Sasek J 

PROVIDER: S-EPMC11426315 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature