The evaluation of automobile interior acceleration sound fused with physiological signal using a hybrid deep neural network

Abstract

The evaluation of automobile sound quality is not only related to the inherent properties of the sound, but also to the psychological and physiological state of the evaluator, which makes the evaluation of sound quality become an interdisciplinary research. However, there are some deficiencies regarding the existing studies as follows: (1) it is imprecise enough to visualize the preference of evaluators for sound on the scale; (2) the evaluators without acoustic experience are prone to deviations in their evaluations, resulting in the universally and unapplicable results; (3) the automobile sound quality cannot be fully reflected only by physical and psychological parameters, and most of the existing evaluation models of sound quality are only characterized by shallow architectures. To alleviate the above flaws, a hybrid deep neural network (HDNN) is constructed to achieve the evaluation of automobile interior acceleration sound fused with physiological signals in this paper. An EEG test paradigm under the stimulation of automobile sound is meanwhile designed for a feasibility confirmation of the proposed method. In addition, the acquired EEG datum are respectively analyzed from two perspectives: the manual extraction of typical EEG features and the adaptive extraction of EEG features based on HDNN model. Furthermore, the performance of the proposed HDNN is also validated by comparing with conventional convolutional neural network (CNN) and long short time memory (LSTM). The results indicate that the adaptively extracted EEG features are superior to the manually extracted EEG features, and the proposed HDNN outperforms the CNN and LSTM in terms of accuracy, sensitivity as well as precision, and the optimal accuracy of HDNN is up to 88.1%. Thus, the constructed HDNN contributes to achieving accurate evaluation of automobile sound quality with EEG signal.