Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN

Abstract

The sound quality of automotive transmission noise strongly influences passengers’ psychological and physiological perceptions. To predict the sound quality of automotive transmission noise, a uniform deceleration noise test with two automotive transmissions has been conducted in a semi-anechoic room. All recorded transmission noise signals have been divided into 5 s segments and subsequently evaluated subjectively through the rating scales test by a jury. In addition, a novel prediction method, namely, Mel-Frequency Cepstral Coefficients-based convolutional neural networks (MFCC-CNN), which substitute the softmax classification layer for the linear transform prediction layer at the output of the general CNN’s structure and take MFCC feature map as input, has been proposed to predict the transmission sound quality. MFCC's distinguishing performance on sound quality has been validated. The parameter selection of the MFCC-CNN model has been compared and studied using a grid search. In addition, three conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed MFCC-CNN. The results show the following: (1) In different transmission gears, MFCC features can distinguish different sound quality noises. (2) The accuracy of the proposed MFCC-CNN sound quality prediction approach are better than those of the 3 other referenced methods. (3) The correlation coefficient of prediction value from MFCC-CNN is more than 0.95 and the mean absolute error of prediction value from MFCC-CNN is less than 0.55, which fully meets the need of engineering. Finally, the newly proposed MFCC-CNN approach may be extended to address other vehicle noises in the future.