전문가 지식을 활용한 군집화를 통한 확산 모델 기반 차량 내 소음 증강

초록

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.

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