Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training
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 cl...
| Authors | Yanxiong Li, Guoqing Chen, Qianqian Li et al. |
|---|---|
| Primary category | eess.AS |
| Published | 8 June 2026 |
| Last updated at source | 14 June 2026 |
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