Abstract
This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge and 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 on this ten-class problem with a device-general model, improving to 51.89% when using the available device information.
Abstract (translated)
本文介绍了DCASE 2025挑战赛中的低复杂度声景分类与设备信息任务及其基准系统。延续了前几届（2022-2024年）对低复杂性模型、数据效率和设备差异化的关注，今年的任务引入了一个关键变化：在推理时提供录音设备的信息。这使得开发具有特定设备特性的模型成为可能，这些特性反映了实际部署场景中，在设计模型时会考虑到底层硬件的情况。训练集匹配了对应DCASE 2024挑战赛使用的25%子集，并且不限制使用外部数据的范围，突出了迁移学习作为主要研究课题的重要性。基准系统在不考虑设备信息的情况下对于这个十类问题达到了50.72%的准确率，在利用可用设备信息时这一数字提升到了51.89%。
URL
https://arxiv.org/abs/2505.01747
