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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 sound...

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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: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900853/
https://www.ncbi.nlm.nih.gov/pubmed/36747788
http://dx.doi.org/10.1101/2023.01.24.525459
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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 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.
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spelling pubmed-99008532023-02-07 DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals Colligan, Thomas Irish, Kayla Emlen, Douglas J. Wheeler, Travis J. bioRxiv 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 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. Cold Spring Harbor Laboratory 2023-01-26 /pmc/articles/PMC9900853/ /pubmed/36747788 http://dx.doi.org/10.1101/2023.01.24.525459 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle 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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900853/
https://www.ncbi.nlm.nih.gov/pubmed/36747788
http://dx.doi.org/10.1101/2023.01.24.525459
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