# Few-shot acoustic event detection via meta-learning

arXiv: https://arxiv.org/abs/2002.09143 · HTML: https://mlsanity.com/paper/2002.09143/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

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

- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)
- [Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition](https://mlsanity.com/paper/2107.07029/) (2021)
- [ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users](https://mlsanity.com/paper/2202.11134/) (2022)
- [Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection](https://mlsanity.com/paper/2205.11738/) (2022)
- [HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification](https://mlsanity.com/paper/2302.14204/) (2023)
- [Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review](https://mlsanity.com/paper/2408.10330/) (2024)
- [Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning](https://mlsanity.com/paper/2505.16220/) (2025)
- [Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining](https://mlsanity.com/paper/2102.08074/) (2021)
- [HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones](https://mlsanity.com/paper/2303.07538/) (2023)
- [Plug-and-Play Multilingual Few-shot Spoken Words Recognition](https://mlsanity.com/paper/2305.03058/) (2023)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)

## Abstract (arXiv)

We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few-shot classification problem. Compared to supervised baselines, meta-learning models achieve superior performance, thus showing its effectiveness on generalization to new audio events. Our analysis including impact of initialization and domain discrepancy further validate the advantage of meta-learning approaches in few-shot AED.