TY  - JOUR
T1  - Diffusion Model-based In-vehicle Noise Augmentation through Expert Knowledge-guided Clustering
AU  - Choi, Seok-Hun 
AU  - Baek, Mugeun 
AU  - Buu, Seok-Jun 
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.9.771
KW  - diffusion models
KW  - generative deep learning
KW  - data augmentation
KW  - acoustic classification
KW  - in-vehicle noise classification
AB  - 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.