提出可增可减类别的少样本音频分类方法，提升模型适应能力。
Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training
- 用原型自适应动态调整分类器结构以应对类别变化
- 在三个数据集上平均准确率超越已有方法
- 适合需要灵活增减类别的实时音频识别场景
在少样本类别增量音频分类任务中，传统方法假设类别数始终增加，但实际中类别数可能增或减。本文研究少样本类别可变增量音频分类（FCIAC）问题，提出基于原型自适应与伪类别可变训练的方法。模型由编码器和分类器组成，分类器通过随类别变化而动态调整结构的类别可变原型适配网络初始化。此外，设计了伪类别可变训练策略，增强模型对类别变动的适应性。在三个公开数据集上的实验表明，该方法在平均准确率上优于现有方法。代码已开源：https://github.com/cgq2971-afk/FCIAC。
原文摘要 · Abstract (English)
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.
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