Bird detection in audio: a survey and a challenge
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2021DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detectioncited by 3
- 2025An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeoncited by 2
- 2021What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysiscited by 0
- 2024Advanced Framework for Animal Sound Classification With Features Optimizationcited by 0
- 2025An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon22 Apr 2025
- 2024Advanced Framework for Animal Sound Classification With Features Optimization3 Jul 2024
- 2021What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis22 Jul 2021
- 2021DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection29 Jun 2021