# MetaAudio: A Few-Shot Audio Classification Benchmark

arXiv: https://arxiv.org/abs/2204.02121 · HTML: https://mlsanity.com/paper/2204.02121/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [Meta-Curvature](https://mlsanity.com/paper/1902.03356/) (2019)
- [Few-Shot Learning with Class Imbalance](https://mlsanity.com/paper/2101.02523/) (2021)
- [Learning to match transient sound events using attentional similarity for few-shot sound recognition](https://mlsanity.com/paper/1812.01269/) (2018)
- [Few-shot acoustic event detection via meta-learning](https://mlsanity.com/paper/2002.09143/) (2020)
- [A Channel Coding Benchmark for Meta-Learning](https://mlsanity.com/paper/2107.07579/) (2021)
- [A Study of Few-Shot Audio Classification](https://mlsanity.com/paper/2012.01573/) (2020)

## Built on by (most cited first, 24 of 24)

- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [Self-Supervised Learning for Few-Shot Bird Sound Classification](https://mlsanity.com/paper/2312.15824/) (2023)
- [Amortised Invariance Learning for Contrastive Self-Supervision](https://mlsanity.com/paper/2302.12712/) (2023)
- [Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection](https://mlsanity.com/paper/2309.08971/) (2023)
- [Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data](https://mlsanity.com/paper/2410.02056/) (2024)
- [Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds](https://mlsanity.com/paper/2403.09598/) (2024)
- [Stepping Forward on the Last Mile](https://mlsanity.com/paper/2411.04036/) (2024)
- [MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations](https://mlsanity.com/paper/2305.17191/) (2023)
- [HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification](https://mlsanity.com/paper/2302.14204/) (2023)
- [Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning](https://mlsanity.com/paper/2305.13080/) (2023)
- [Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning](https://mlsanity.com/paper/2309.00878/) (2023)
- [On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification](https://mlsanity.com/paper/2402.01274/) (2024)
- [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)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)
- [EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments](https://mlsanity.com/paper/2405.02119/) (2024)
- [A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition](https://mlsanity.com/paper/2410.04633/) (2024)
- [Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano](https://mlsanity.com/paper/2410.23325/) (2024)
- [MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition](https://mlsanity.com/paper/2502.19071/) (2025)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)
- [Overcoming Labelled Data Scarcity for Defect Classification in Scanning Tunneling Microscopy](https://mlsanity.com/paper/2506.01678/) (2025)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)
- [Sample-Conditioned Representation Selection for Audio Few-Shot Learning](https://mlsanity.com/paper/2609.17076/) (2026)

## Abstract (arXiv)

Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification. This work aims to alleviate this reliance on image-based benchmarks by offering the first comprehensive, public and fully reproducible audio based alternative, covering a variety of sound domains and experimental settings. We compare the few-shot classification performance of a variety of techniques on seven audio datasets (spanning environmental sounds to human-speech). Extending this, we carry out in-depth analyses of joint training (where all datasets are used during training) and cross-dataset adaptation protocols, establishing the possibility of a generalised audio few-shot classification algorithm. Our experimentation shows gradient-based meta-learning methods such as MAML and Meta-Curvature consistently outperform both metric and baseline methods. We also demonstrate that the joint training routine helps overall generalisation for the environmental sound databases included, as well as being a somewhat-effective method of tackling the cross-dataset/domain setting.