# Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes

arXiv: https://arxiv.org/abs/2305.19539 · HTML: https://mlsanity.com/paper/2305.19539/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 10 of 10)

- [Gradient-Semantic Compensation for Incremental Semantic Segmentation](https://mlsanity.com/paper/2307.10822/) (2023)
- [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://mlsanity.com/paper/2402.12035/) (2024)
- [Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2502.08181/) (2025)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)
- [EAViT: External Attention Vision Transformer for Audio Classification](https://mlsanity.com/paper/2408.13201/) (2024)
- [Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training](https://mlsanity.com/paper/2504.08412/) (2025)
- [When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2603.25115/) (2026)
- [Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training](https://mlsanity.com/paper/2606.08898/) (2026)
- [Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement](https://mlsanity.com/paper/2607.26607/) (2026)
- [SPECTRA: Subspace-Preserving Embedding Calibration, Transport, and Replay for Fully Few-Shot Class-Incremental Audio Classification](https://mlsanity.com/paper/2608.25054/) (2026)

## Abstract (arXiv)

Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification systems need to be retrained with abundant labeled samples of all audio classes for recognizing base (initial) and novel audio classes. If novel audio classes continue to appear, the existing methods for audio classification will be inefficient and even infeasible. In this work, we propose a method for few-shot class-incremental audio classification, which can continually recognize novel audio classes without forgetting old ones. The framework of our method mainly consists of two parts: an embedding extractor and a classifier, and their constructions are decoupled. The embedding extractor is the backbone of a ResNet based network, which is frozen after construction by a training strategy using only samples of base audio classes. However, the classifier consisting of prototypes is expanded by a prototype adaptation network with few samples of novel audio classes in incremental sessions. Labeled support samples and unlabeled query samples are used to train the prototype adaptation network and update the classifier, since they are informative for audio classification. Three audio datasets, named NSynth-100, FSC-89 and LS-100 are built by choosing samples from audio corpora of NSynth, FSD-MIX-CLIP and LibriSpeech, respectively. Results show that our method exceeds baseline methods in average accuracy and performance dropping rate. In addition, it is competitive compared to baseline methods in computational complexity and memory requirement. The code for our method is given at https://github.com/vinceasvp/FCAC.