Research on extraction and identification of automotive wind noise based on LSTM combined road and drum experiments

Wind noise is a significant source of noise when vehicles are driven at high speeds. While wind tunnel experiments are commonly method to study real automotive wind noise, anechoic wind tunnel laboratory are prohibitive for automakers on account of their high costs and limited experimental period. Therefore, this paper proposes to extract automotive wind noise through combining road experiment and drum experiment, define significant wind noise prime regions, and verify its effectiveness. Subsequently, the Long Short-Term Memory Neural Network algorithm (LSTM) is employed to reveal the complex nonlinear relationship between wind noise and its impact areas, in order to establish an identification model for automotive wind noise. The performance of the model is then compared with Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) models. The results indicate that the LSTM wind noise identification model exhibits higher accuracy, shorter training time, and stronger generalization ability, thus demonstrating the superiority of this model.