Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task
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| author | Casciotti, Riccardo Mulimani, Manjunath Harju, Manu Jensen, Jesper Rindom Mesaros, Annamaria |
| author_facet | Casciotti, Riccardo Mulimani, Manjunath Harju, Manu Jensen, Jesper Rindom Mesaros, Annamaria |
| contents | 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. Participants will train a system to learn ten sound classes in three different domains, with learning at each incremental task not having access to previous task data. Submitted systems will be ranked by the overall average accuracy calculated over the three domains. The provided baseline system obtains a modest performance of 44.9\% accuracy over the three domains, mostly due to erroneous inference of the domain for the test sample. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02173 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task Casciotti, Riccardo Mulimani, Manjunath Harju, Manu Jensen, Jesper Rindom Mesaros, Annamaria Audio and Speech Processing 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. Participants will train a system to learn ten sound classes in three different domains, with learning at each incremental task not having access to previous task data. Submitted systems will be ranked by the overall average accuracy calculated over the three domains. The provided baseline system obtains a modest performance of 44.9\% accuracy over the three domains, mostly due to erroneous inference of the domain for the test sample. |
| title | Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2606.02173 |