# Learning to match transient sound events using attentional similarity for few-shot sound recognition

arXiv: https://arxiv.org/abs/1812.01269 · HTML: https://mlsanity.com/paper/1812.01269/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

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

- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection](https://mlsanity.com/paper/2205.11738/) (2022)
- [MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations](https://mlsanity.com/paper/2305.17191/) (2023)
- [Deep Oscillatory Neural Network](https://mlsanity.com/paper/2405.03725/) (2024)
- [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)
- [Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification](https://mlsanity.com/paper/2410.05302/) (2024)
- [Prototypical Contrastive Learning For Improved Few-Shot Audio Classification](https://mlsanity.com/paper/2509.10074/) (2025)
- [Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations](https://mlsanity.com/paper/2305.02382/) (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)

In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets shows that the proposed module consistently improves the performance of five different metric-based learning methods for few-shot sound recognition. The relative improvement ranges from +4.1% to +7.7% for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events.