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

Thomas Colligan, Kayla Irish, Douglas J. Emlen, Travis J. Wheeler

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere0288172
Pages (from-to)e0288172
JournalPLoS ONE
Issue number7 July
StatePublished - Jul 2023


  • Animals
  • Deep Learning
  • Uncertainty
  • Reproducibility of Results
  • Acoustics
  • Whales
  • Vocalization, Animal


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