Domain-incremental audio classification using domain-specific experts and prototype classifier
This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data at once. We approached domain-incremental learning (DIL) as a frozen-feature replay problem. At each incremental stage, one or two compact experts are trained and then kept fixed; at the final stage, the penultimate features from all frozen experts are concatenated and used to train a lightweight per-class prototype classifier solely on cached features. This design prevents catastrophic forgetting by preserving each expert models at inference. To retain earlier-domain kno...
| Authors | Jongyeon Park, Do-Hyeon Lim, Sang-won Park et al. |
|---|---|
| Primary category | eess.AS |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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