# Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training

arXiv: https://arxiv.org/abs/2606.08898 · HTML: https://mlsanity.com/paper/2606.08898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [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)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Few-Shot Incremental Learning with Continually Evolved Classifiers](https://mlsanity.com/paper/2104.03047/) (2021)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [Multiscale Audio Spectrogram Transformer for Efficient Audio Classification](https://mlsanity.com/paper/2303.10757/) (2023)
- [Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes](https://mlsanity.com/paper/2305.19539/) (2023)
- [Sound event detection via dilated convolutional recurrent neural networks](https://mlsanity.com/paper/1911.10888/) (2019)
- [Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes](https://mlsanity.com/paper/2305.18045/) (2023)
- [Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds](https://mlsanity.com/paper/2107.11506/) (2021)
- [Few-shot Class-incremental Audio Classification Using Stochastic Classifier](https://mlsanity.com/paper/2306.02053/) (2023)
- [Domestic activities clustering from audio recordings using convolutional capsule autoencoder network](https://mlsanity.com/paper/2105.03583/) (2021)
- [HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification](https://mlsanity.com/paper/2302.14204/) (2023)
- [Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet](https://mlsanity.com/paper/2306.02054/) (2023)
- [Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier](https://mlsanity.com/paper/2506.18406/) (2025)

## Abstract (arXiv)

In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the number of classes generally increases or decreases in practice. In this paper, we investigate a problem of Few-shot Class-variable Incremental Audio Classification (FCIAC), in which the number of classes increases or decreases. We propose a FCIAC method using prototype adaptation and pseudo class-variable training. The model in our method consists of an encoder and a classifier. The classifier is initialized by a class-variable prototype adaptation network, whose structure dynamically changes with the change of classes. In addition, we design a pseudo class-variable training strategy to enhance the model's adaptability to changing classes. Experiments on three public datasets show that our method exceeds previous methods in average accuracy. The code is at: https://github.com/cgq2971-afk/FCIAC.