Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910196834500608 |
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| author | Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Widmer, Gerhard |
| author_facet | Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Widmer, Gerhard |
| contents | This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge, along with its baseline system. Continuing the focus on low-complexity models, data efficiency, and device mismatch from previous editions (2022-2024), this year's task introduces a key change: recording device information is now provided at inference time. This enables the development of device-specific models that leverage device characteristics-reflecting real-world deployment scenarios in which a model is designed with awareness of the underlying hardware. The training set matches the 25% subset used in the corresponding DCASE 2024 challenge, with no restrictions on external data use, highlighting transfer learning as a central topic. The baseline achieves 50.72% accuracy with a device-agnostic model, improving to 51.89% when incorporating device-specific fine-tuning. The task attracted 31 submissions from 12 teams, with 11 teams outperforming the baseline. The top-performing submission achieved an accuracy gain of more than 8 percentage points over the baseline on the evaluation set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Widmer, Gerhard Audio and Speech Processing Sound This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge, along with its baseline system. Continuing the focus on low-complexity models, data efficiency, and device mismatch from previous editions (2022-2024), this year's task introduces a key change: recording device information is now provided at inference time. This enables the development of device-specific models that leverage device characteristics-reflecting real-world deployment scenarios in which a model is designed with awareness of the underlying hardware. The training set matches the 25% subset used in the corresponding DCASE 2024 challenge, with no restrictions on external data use, highlighting transfer learning as a central topic. The baseline achieves 50.72% accuracy with a device-agnostic model, improving to 51.89% when incorporating device-specific fine-tuning. The task attracted 31 submissions from 12 teams, with 11 teams outperforming the baseline. The top-performing submission achieved an accuracy gain of more than 8 percentage points over the baseline on the evaluation set. |
| title | Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2505.01747 |