mlsanityData to 7 Oct 2026

Few-shot bioacoustic event detection at the DCASE 2022 challenge

arXiv 2207.07911 ↗First posted 14 Jul 2022cs.SD cs.LG eess.AScited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 14 Jul 2022.

Builds on

8 of 30 references.

References · 8

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  3. 2018Low-Shot Learning from Imaginary Datacited by 181
  4. 2021Computational bioacoustics with deep learning: a review and roadmapcited by 78
  5. 2021HumBugDB: A Large-scale Acoustic Mosquito Datasetcited by 15
  6. 2020Few-Shot Drum Transcription in Polyphonic Musiccited by 7
  7. 2021Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceiverscited by 5
  8. 2022Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated datasetcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 3; 2026: 3.

13 papers.

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  1. 2023Unsupervised classification to improve the quality of a bird song recording datasetcited by 10
  2. 2023AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoringcited by 10
  3. 2024Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacousticscited by 7
  4. 2024From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learningcited by 4
  5. 2023Few-shot bioacoustic event detection at the DCASE 2023 challengecited by 3
  6. 2023Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learningcited by 2
  7. 2025Synthetic data enables context-aware bioacoustic sound event detectioncited by 2
  8. 2025Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detectioncited by 2
  9. 2026SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classificationcited by 2
  10. 2022Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothingcited by 1
  11. 2025Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Trainingcited by 0
  12. 2026A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classificationcited by 0
1 more
  1. 2026OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizationscited by 0