Bird detection in audio: a survey and a challenge
This work aims to improve biological monitoring by enabling more efficient and scalable bird detection from audio data, though it is incremental as it builds on existing survey and challenge frameworks.
The paper addresses the need for tuning-free and species-agnostic automatic bird sound detection by reviewing state-of-the-art methods and introducing new datasets and an IEEE research challenge to facilitate the development of fully automatic algorithms.
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.