# Few-shot bioacoustic event detection at the DCASE 2022 challenge

arXiv: https://arxiv.org/abs/2207.07911 · HTML: https://mlsanity.com/paper/2207.07911/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Low-Shot Learning from Imaginary Data](https://mlsanity.com/paper/1801.05401/) (2018)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [HumBugDB: A Large-scale Acoustic Mosquito Dataset](https://mlsanity.com/paper/2110.07607/) (2021)
- [Few-Shot Drum Transcription in Polyphonic Music](https://mlsanity.com/paper/2008.02791/) (2020)
- [Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers](https://mlsanity.com/paper/2107.13616/) (2021)
- [Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset](https://mlsanity.com/paper/2207.05393/) (2022)

## Built on by (most cited first, 13 of 13)

- [Unsupervised classification to improve the quality of a bird song recording dataset](https://mlsanity.com/paper/2302.07560/) (2023)
- [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023)
- [Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics](https://mlsanity.com/paper/2404.16436/) (2024)
- [From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning](https://mlsanity.com/paper/2403.08525/) (2024)
- [Few-shot bioacoustic event detection at the DCASE 2023 challenge](https://mlsanity.com/paper/2306.09223/) (2023)
- [Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning](https://mlsanity.com/paper/2309.00878/) (2023)
- [Synthetic data enables context-aware bioacoustic sound event detection](https://mlsanity.com/paper/2503.00296/) (2025)
- [Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection](https://mlsanity.com/paper/2505.20956/) (2025)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)
- [Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing](https://mlsanity.com/paper/2210.10526/) (2022)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)
- [A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification](https://mlsanity.com/paper/2608.14824/) (2026)
- [OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations](https://mlsanity.com/paper/2609.34839/) (2026)

## Abstract (arXiv)

Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a need to annotate very long recordings but the expert annotator time is limited. This paper presents an overview of the second edition of the few-shot bioacoustic sound event detection task included in the DCASE 2022 challenge. A detailed description of the task objectives, dataset, and baselines is presented, together with the main results obtained and characteristics of the submitted systems. This task received submissions from 15 different teams from which 13 scored higher than the baselines. The highest F-score was of 60% on the evaluation set, which leads to a huge improvement over last year's edition. Highly-performing methods made use of prototypical networks, transductive learning, and addressed the variable length of events from all target classes. Furthermore, by analysing results on each of the subsets we can identify the main difficulties that the systems face, and conclude that few-show bioacoustic sound event detection remains an open challenge.