Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training
Researchers have introduced a new method for few-shot class-variable incremental audio classification, addressing scenarios where the number of audio classes can both increase and decrease over time. Their approach utilizes a dynamic classifier initialized with a class-variable prototype adaptation network and incorporates a pseudo class-variable training strategy to improve adaptability. Experiments on three public datasets demonstrate that this novel method outperforms existing techniques in average accuracy. AI
IMPACT Introduces a novel approach to handle dynamic class changes in audio classification, potentially improving real-world AI system adaptability.