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.

翻译：在少样本类别增量式音频分类任务中，通常假设类别数量始终增加而不考虑减少的可能性。然而在实际应用中，类别数量往往呈现增减变化。本文研究了一种少样本类别变体增量式音频分类问题，其中类别数量可能增加或减少。我们提出了一种采用原型自适应与伪类别变体训练的FCIAC方法。该方法模型由编码器和分类器组成，分类器通过类别变体原型自适应网络初始化，其结构随类别变化动态调整。此外，我们设计了伪类别变体训练策略以增强模型对动态类别的适应能力。在三个公开数据集上的实验表明，本方法在平均准确率上优于现有方法。代码地址：https://github.com/cgq2971-afk/FCIAC。