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 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.

Automated summary

Recordings of animal sounds are valuable for studying animal communication, behavior, and diversity. The software package DISCO provides an efficient and accurate way to label elements in these recordings, improving analysis throughput and reproducibility. It also includes tools for labeling training data and visualizing the resulting labels.