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arXiv · 2305.18045

Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes

Wei Xie·Yanxiong Li·Qianhua He·Wenchang Cao·Tuomas Virtanen
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Abstract

New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the new problem of few-shot class-incremental audio classification. This study aims to enable a model to continuously recognize new classes of sounds with a few training samples of new classes while remembering the learned ones. To this end, we propose a method to generate discriminative prototypes and use them to expand the model's classifier for recognizing sounds of new and learned classes. The model is first trained with a random episodic training strategy, and then its backbone is used to generate the prototypes. A dynamic relation projection module refines the prototypes to enhance their discriminability. Results on two datasets (derived from the corpora of Nsynth and FSD-MIX-CLIPS) show that the proposed method exceeds three state-of-the-art methods in average accuracy and performance dropping rate.

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Wei Xie, Yanxiong Li, Qianhua He, Wenchang Cao, Tuomas Virtanen. 2023-05-29. Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes. https://arxiv.org/abs/2305.18045

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