APRIL: Ambiguity-Aware Expert Routing for Respiratory Sound Domain-Incremental Learning
Automated respiratory sound classification enables continuous, non-invasive respiratory assessment, yet long-term deployment remains challenging due to domain shifts from evolving recording devices and acquisition periods. When privacy and governance regulations prohibit replaying historical data, sequential learning easily overwrites previously acquired knowledge. Preserving dedicated domain experts prevents parameter interference, but selecting the correct expert at inference is difficult under overlapping acoustic distributions and absent domain labels. To resolve this dilemma, we propose APRIL, an exemplar-free domain-incremental framework for RSC. Built on a shared frozen BEATs backb...
| Authors | Xinhong Li, Weiping Tu |
|---|---|
| Primary category | cs.SD |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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