Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task

Abstract

This paper presents the Domain-Agnostic Incremental Learning for Audio Classification Task of the DCASE 2026 Challenge. Incremental learning refers to sequentially learning new tasks with the same system while maintaining its knowledge and performance on the previously learned task. Domain-incremental learning for sound classification refers to learning the same sound classes but in different acoustic domains, and was formalized as a data challenge for the first time in DCASE 2026. The task required participants to train a system to learn ten sound classes in three different domains, starting from a baseline system trained on the first domain. At each incremental task, learning had to be done without access to previous task data. The systems were ranked by the overall average accuracy calculated over the three domains. The task generated high interest and received submissions from 21 teams, with most submissions outperforming the provided baseline system. The highest performance of 79.6% was obtained using a combination of continual learning strategies that resulted in a balanced accuracy across domains, hence a good balance of retaining old and learning new information.

arXiv commentDCASE 2026 Workshop