Bird detection in audio: a survey and a challenge

TL;DR

This paper reviews current methods for automatic bird sound detection, highlights the need for tuning-free, species-agnostic solutions, and introduces new datasets and a challenge to foster development of fully automatic detection algorithms.

Contribution

It provides a comprehensive survey of bird sound detection techniques and launches a new challenge with datasets to promote tuning-free, species-agnostic detection methods.

Findings

Identification of the need for tuning-free, species-agnostic detection methods

Introduction of new datasets for bird sound detection

Proposal of an IEEE research challenge to advance the field

Abstract

Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.