MLLab PhD students organise DCASE challenge task on computational bioacoustics

Machine Listening Lab PhD students Ines Nolasco, Shubhr Singh, and Jinhua Liang are organising the task on Few-shot Bioacoustic Event Detection as part of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2024).

This task addresses a real need from animal researchers by providing a well-defined, constrained yet highly variable domain to evaluate machine learning methodology. It aims to advance the study of audio signal processing and deep learning in the low-resource scenario, particularly in domain adaptation and few-shot learning. Datasets will be released on 1st June 2024, with the challenge deadline being on 15 June 2024.

Can you build a system that detects an animal sound with only 5 examples? Let’s liaise to push the boundary of computational bioacoustics and machine listening!