TY - JOUR
T1 - DISCO
T2 - A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
AU - Colligan, Thomas
AU - Irish, Kayla
AU - Emlen, Douglas J.
AU - Wheeler, Travis J.
N1 - Copyright: © 2023 Colligan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Animals
KW - Deep Learning
KW - Uncertainty
KW - Reproducibility of Results
KW - Acoustics
KW - Whales
KW - Vocalization, Animal
UR - http://www.scopus.com/inward/record.url?scp=85165932836&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c6ed103b-76a1-3b50-95a2-7132bc906ec8/
U2 - 10.1371/journal.pone.0288172
DO - 10.1371/journal.pone.0288172
M3 - Article
C2 - 37494341
AN - SCOPUS:85165932836
SN - 1932-6203
VL - 18
SP - e0288172
JO - PLoS ONE
JF - PLoS ONE
IS - 7 July
M1 - e0288172
ER -