# Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

arXiv: https://arxiv.org/abs/2607.01297 · HTML: https://mlsanity.com/paper/2607.01297/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 67 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling](https://mlsanity.com/paper/2207.04179/) (2022)
- [A Closer Look at Few-shot Classification Again](https://mlsanity.com/paper/2301.12246/) (2023)
- [Audio-Visual Class-Incremental Learning](https://mlsanity.com/paper/2308.11073/) (2023)
- [Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2012.13073/) (2020)
- [Meta-Learning with a Geometry-Adaptive Preconditioner](https://mlsanity.com/paper/2304.01552/) (2023)
- [Glocal Energy-based Learning for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2304.11855/) (2023)
- [Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes](https://mlsanity.com/paper/2305.19539/) (2023)
- [Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems](https://mlsanity.com/paper/2306.02161/) (2023)
- [Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting](https://mlsanity.com/paper/2206.13691/) (2022)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Sound Event Detection with Depthwise Separable and Dilated Convolutions](https://mlsanity.com/paper/2002.00476/) (2020)
- [Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes](https://mlsanity.com/paper/2305.18045/) (2023)
- [LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition](https://mlsanity.com/paper/2308.12882/) (2023)
- [MARS: Meta-Learning as Score Matching in the Function Space](https://mlsanity.com/paper/2210.13319/) (2022)
- [Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization](https://mlsanity.com/paper/2305.07132/) (2023)
- [MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks](https://mlsanity.com/paper/2305.10309/) (2023)
- [Few-shot Class-incremental Audio Classification Using Stochastic Classifier](https://mlsanity.com/paper/2306.02053/) (2023)
- [Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration](https://mlsanity.com/paper/2306.05621/) (2023)
- [An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments](https://mlsanity.com/paper/2002.11561/) (2020)
- [Domestic activities clustering from audio recordings using convolutional capsule autoencoder network](https://mlsanity.com/paper/2105.03583/) (2021)
- [openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer](https://mlsanity.com/paper/2202.12349/) (2022)
- [Fully Few-shot Class-incremental Audio Classification Using Expandable Dual-embedding Extractor](https://mlsanity.com/paper/2406.08122/) (2024)

## Built on by (most cited first, 2 of 2)

- [TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios](https://mlsanity.com/paper/2607.06179/) (2026)
- [Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement](https://mlsanity.com/paper/2607.26607/) (2026)

## Abstract (arXiv)

Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes). In this study, we propose a method for Few-shot Open-set Audio Classification (FOAC), which can recognize query samples of seen classes after updating the model using a few support samples, and meanwhile reject query samples from unseen classes. We design a model consisting of an encoder and a classifier. The encoder is the backbone of a ResNet used for extracting embeddings. The classifier consists of prototype generators of few-shot classes and open-set classes. Prototypes of few-shot classes are obtained by fusing the class-discriminative information of support and query embeddings and by assigning larger weighting coefficient to representative part of the support embeddings. One prototype is generated for open-set classes using the proposed prototype generator. The encoder is trained with abundant samples of base classes in supervised manner, and then the prototypes of base classes are generated under the supervision of a joint loss. The classifier is trained using a few samples of few-shot classes in a meta-training way. Three public datasets (LS-100, NSynth-100, and FSC-89) are used to assess the performance of our method. Experiments show that our method has advantage over prior methods in AUROC and accuracy. This advantage has statistical significance for most prior methods. Our method has lower computational complexity than most prior methods. The code is at https://github.com/Jessytan/FOAC-AIFP.