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DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.
Colligan, Thomas; Irish, Kayla; Emlen, Douglas J; Wheeler, Travis J.
Afiliação
  • Colligan T; Department of Pharmacy Practice & Science, University of Arizona, Tucson, AZ, USA.
  • Irish K; Department of Computer Science, University of Montana, Missoula, MT, USA.
  • Emlen DJ; Department of Computer Science, University of Montana, Missoula, MT, USA.
  • Wheeler TJ; Department of Statistics, University of Washington, Seattle, WA, USA.
bioRxiv ; 2023 Jan 26.
Article em En | MEDLINE | ID: mdl-36747788
ABSTRACT
Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we describe our software package for labeling sound elements in recordings of animal sounds and demonstrate its utility on recordings of beetle courtships and whale songs. The software, DISCO, computes sensible confidence estimates and produces labels with high precision and accuracy. In addition to the core labeling software, it provides a simple tool for labeling training data, and a visual system for analysis of resulting labels. DISCO is open-source and easy to install, it works with standard file formats, and it presents a low barrier of entry to use.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos