# Bird detection in audio: a survey and a challenge

arXiv: https://arxiv.org/abs/1608.03417 · HTML: https://mlsanity.com/paper/1608.03417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2106.15190/) (2021)
- [An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon](https://mlsanity.com/paper/2504.16276/) (2025)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [Advanced Framework for Animal Sound Classification With Features Optimization](https://mlsanity.com/paper/2407.03440/) (2024)

## Abstract (arXiv)

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.