Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge

TL;DR

This paper introduces a low-complexity acoustic scene classification task for DCASE 2025, emphasizing device-specific models and transfer learning, with a baseline system and analysis of participant submissions.

Contribution

It presents a new challenge setup that incorporates device information at inference, enabling development of device-aware models for acoustic scene classification.

Findings

Baseline accuracy is 50.72%, improved to 51.89% with device fine-tuning.

11 out of 12 teams outperformed the baseline.

Top submission improved accuracy by over 8 percentage points.

Abstract

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.