The interior sound quality holds a central position in the vehicle quality evaluation system. It shapes the users’ perception of the vehicle and significantly influences consumers’ purchasing decisions. Therefore, it is extremely crucial to accurately assess its quality. Numerous researchers have been dedicated to developing intelligent prediction models to precisely measure the in-vehicle sound quality. The deep convolutional neural network (CNN), due to its excellent ability of automatic feature learning, has been widely applied in the processing and analysis of noise and vibration problems. However, there are two issues in these studies: 1) CNN performs poorly in multi-dimensional feature extraction; 2) CNN has limited adaptability when dealing with dynamic data. To overcome the above problems, an in-vehicle sound quality evaluation model integrating CNN and bidirectional long short-term memory network (Bi-LSTM) was constructed. The results show that the model achieves a maximum prediction accuracy of 96% in the training set, and no significant overfitting occurs, demonstrating the feasibility of the generalization ability and prediction accuracy of the new model.
Zhang et al. (Fri,) studied this question.