Seok-Hun Choi  Mugeun Baek  Seok-Jun Buu

10.5626/JOK.2025.52.9.771

Diffusion models generative deep learning data augmentation acoustic classification in-vehicle noise classification

To ensure vehicle operational safety and enhance user experience, it is crucial to accurately classify in-vehicle noise and detect performance anomalies in advance. However, deep learning-based noise classifiers often struggle in complex acoustic environments, such as those with external noise and internal reverberation. To address these challenges, we propose a novel vehicle noise classification method that integrates diffusion model-based signal augmentation with expert knowledge-guided clustering. This approach synthesizes a variety of challenging in-vehicle acoustic conditions and enhances signal-label associations through automatic label assignment based on expert-defined clusters. As a result, we can create training datasets that closely mirror real-world scenarios. Our experiments demonstrate that this method achieves a classification accuracy of 99.60%, surpassing state-of-the-art classifiers and improving by 0.06 percentage points over existing generative augmentation methods, thereby showcasing the effectiveness of the diffusion-based approach.