Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks
Seok-Jun Bu, Sung‐Bae Cho · 2019
Considering the trend of the vehicle market where the vehicle becomes quieter, in-vehicle rattling noise is significant criterion for the quality of the vehicle. Though the latest deep learning algorithms have been introduced for classifying in-vehicle rattling noise, there are limitations due to impulsive and transient nature of rattling noise and reflective and refractive characteristics of in-vehicle environment. In this paper, we propose a novel beamforming method that extracts intra-interchannel spatial features by parameterizing the optimal beamforming weights including Direction-of-Arrival (DOA) function to overcome the addressed problem. The proposed method outperformed the existing deep learning algorithms with 0.9270 accuracy and verified by 10-fold cross validation and chi-squared test. In addition, it is shown that the time cost for classification of rattling noise is appropriate for real-time classification as a side-effect of using convolution-pooling operations.