Bird detection in audio: a survey and a challenge

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.

Slightly extended preprint of paper accepted for MLSP 2016

References in corpus (1)

• Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

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• What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis
• Automated bird sound recognition in realistic settings