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DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals

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 elemen...

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Detalles Bibliográficos
Autores principales: Colligan, Thomas, Irish, Kayla, Emlen, Douglas J., Wheeler, Travis J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370718/
https://www.ncbi.nlm.nih.gov/pubmed/37494341
http://dx.doi.org/10.1371/journal.pone.0288172
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author Colligan, Thomas
Irish, Kayla
Emlen, Douglas J.
Wheeler, Travis J.
author_facet Colligan, Thomas
Irish, Kayla
Emlen, Douglas J.
Wheeler, Travis J.
author_sort Colligan, Thomas
collection PubMed
description 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.
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spelling pubmed-103707182023-07-27 DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals Colligan, Thomas Irish, Kayla Emlen, Douglas J. Wheeler, Travis J. PLoS One Research Article 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. Public Library of Science 2023-07-26 /pmc/articles/PMC10370718/ /pubmed/37494341 http://dx.doi.org/10.1371/journal.pone.0288172 Text en © 2023 Colligan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Colligan, Thomas
Irish, Kayla
Emlen, Douglas J.
Wheeler, Travis J.
DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title_full DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title_fullStr DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title_full_unstemmed DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title_short DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
title_sort disco: a deep learning ensemble for uncertainty-aware segmentation of acoustic signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370718/
https://www.ncbi.nlm.nih.gov/pubmed/37494341
http://dx.doi.org/10.1371/journal.pone.0288172
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