Few-shot class-incremental audio classification via discriminative prototype learning.

In real-world scenarios, new audio classes with insufficient samples usually emerge continually, which motivates the study of few-shot class-incremental audio classification (FCAC) in this paper. FCAC aims to enable the model to recognize new audio classes while remembering the base ones continually. To solve the FCAC problem, the discriminability of the prototypes is vital to the model's classification performance. Thus, we proposed a method to learn the discriminative prototypes from two aspects. First, since the generalization ability of the embedding module (EM) significantly affects the discriminability of the prototypes, the proposed method employs a scheme of pseudo-episodic incremental training to train the EM by simulating the test scenario. Second, to enable the model to achieve a balanced classification performance on both base and new audio classes, the proposed method employs a selective-attention module to adjust different prototypes to enhance their discriminability. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in solving the FCAC problem. Notably, the proposed method achieves a comprehensive performance score (CPS) of 87.82% and 59.25% on the Neural Synthesis musical notes of 100 classes (NSynth-100) and Free sound clips of 89 classes (FSC-89) datasets, respectively, which outperforms the comparison methods. Our code is available at https://github.com/chester-w-xie/DPL_FCAC.