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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.
Affiliation
  • Colligan T; College of Pharmacy, University of Arizona, Tucson, AZ, United States of America.
  • Irish K; Department of Computer Science, University of Montana, Missoula, MT, United States of America.
  • Emlen DJ; Department of Computer Science, University of Montana, Missoula, MT, United States of America.
  • Wheeler TJ; Department of Statistics, University of Washington, Seattle, WA, United States of America.
PLoS One ; 18(7): e0288172, 2023.
Article in En | MEDLINE | ID: mdl-37494341
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 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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Animals Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2023 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Animals Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2023 Document type: Article Affiliation country: United States