Adaptive Embedding Fusion with Contrastive Learning for Robust Fully Few-Shot Class-Incremental Audio Classification

Fully few-shot class-incremental audio classification (FFCAC) addresses real-world scenarios where new classes have only limited data. The Expandable Dual-embedding Extractor (EDE) is an effective baseline for this task, but it requires progressively expanding embeddings as new classes arrive, leading to increased training and inference complexity. To overcome this limitation, we propose Adaptive Embedding Fusion EDE (AEF-EDE), which prevents embedding expansion and improves model efficiency, while further leveraging contrastive learning to achieve significant performance gains. Experiments on LS-100, NSynth-100, and FSC-89, along with ablation studies, demonstrate the effectiveness of our approach.