DISCO Implements Sound Classification Obediently

This tool annotates sound files using neural networks. It uses a 1D architecture based on U-Net with additional post-processing heuristics including a Hidden Markov Model.

DISCO is ideal for long streams of sound that need to be classified over time, producing output fully compatible with The Cornell Lab of Ornithology's sound tool RAVEN. Work is currently underway to annotate short samples of data with a single label. DISCO began jointly with the University of Montana's Emlen Lab as an annotator for Japanese and Taiwanese Rhinoceros Beetle courtship songs, but it now generalizes to any kind of recording.

Quickstart

Install requires python version 3.8.1. Clone this repo and run:

flit install -s
pip install -r requirements.txt

Tutorial

Visit this link for a thorough setup tutorial.

Learn more about how to use the tools provided in this package here.