Sound quality prediction of vehicle interior noise using deep belief networks. (1st December 2016)

• Record Type: Journal Article
• Authors: Huang, Hai B.; Huang, Xiao R.; Li, Ren X.; Lim, Teik C.; Ding, Wei P.
• Is Part Of: Applied acoustics. Volume 113(2016)
• Page Start: 149
• Page End: 161
• Publication Date: 2016-12-01
• Subjects: Sound quality -- Vehicle interior noise -- Deep belief networks -- Machine learning -- Feature fusion; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2016.06.021
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: A regression-based deep belief network which combined with SVM has been proposed. The DBN approach outperforms the 4 other conventional methods. The multiple-feature fusing process can strongly affect the prediction performance. The unsupervised pre-training process can enhance the information fusing ability. Abstract: The sound quality of vehicle interior noise strongly influences passengers' psychological and physiological perceptions. To predict the sound quality of interior noise, a vehicle road test with four compact cars has been conducted. All recorded interior noise signals have been denoised via a discrete wavelet transform (DWT) denoising procedure and subsequently evaluated subjectively through the anchor semantic differential (ASD) test by a jury. In addition, a novel prediction method, namely, regression-based deep belief networks (DBNs), which substitute the support vector regression (SVR) layer for the linear softmax classification layer at the top of the general DBN's structure, has been proposed to predict the interior sound quality. The parameter selection of the DBN model has been compared and studied using a grid search. In addition, four conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed DBNs. Furthermore, the feature fusion ability of DBNs has been studied by varying the amount of information that the dataset offers. The results show the following: (1) The accuracy and robustness of the proposed DBN-based sound quality prediction approach are better than those of the 4 other referenced methods. (2) The multiple-feature fusing process can strongly affect the prediction performance. (3) Finally, the unsupervised pre-training process of the DBNs can enhance the information fusing ability. Finally, the newly proposed regression-based DBN approach may be extended to address other vehicle noises in the future.