Bird detection in audio: a survey and a challenge

TL;DR

The paper surveys automatic bird sound detection, identifying a critical need for tuning-free and species-agnostic methods in large-scale audio monitoring. It outlines task paradigms and a spectrum of techniques from energy-based VAD-like detectors to template matching, HMMs, and emerging data-driven approaches, while discussing practical challenges such as noise and calibration. To accelerate progress, the authors introduce two public datasets (Warblr and TREE/CEZ) and an IEEE Bird Audio Detection challenge focusing on presence/absence in 10-second clips, aiming to foster generalizable, tuning-free solutions. A baseline MFCC+GMM system achieves ~79% AUC on initial data, illustrating both potential and the remaining gap, and the paper highlights future directions including deep learning and meta-algorithms to combine detectors for robust, scalable bird monitoring.

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.

Figures (1)

• Figure 1: Task paradigms for bird detection. The final column gives a rough ordering in ascending complexity/difficulty.