让模型在推理时利用设备信息，提升声景分类精度。
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
- 引入设备信息辅助分类，支持设备定制化建模。
- 基线模型准确率达50.72%，设备微调后提升至51.89%。
- 适合关注真实设备部署与低复杂度模型的研究者。
本文介绍了 DCASE 2025 挑战赛中的低复杂度声景分类任务（含基线系统），延续了以往（2022–2024）对低复杂度模型、数据效率和设备不匹配的关注。本年度关键变化在于：推理阶段提供录音设备信息，使模型可基于设备特性构建设备特定分类器，更贴近实际部署场景。训练集采用与对应 2024 年挑战赛相同的 25% 子集，不限制外部数据使用，凸显迁移学习的重要性。基线模型为设备无关模型，准确率为 50.72%，通过设备特定微调提升至 51.89%。共有 12 支队伍提交 31 项成果，其中 11 支队伍优于基线。最佳方案在评测集上较基线提升超 8 个百分点。
原文摘要 · Abstract (English)
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.
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