电动轿车警示声设计方法研究

Aiming at the problem that electric vehicle in low-speed driving is too quiet to arouse the vigilance of surrounding vehicles and pedestrians, the design method of a warning sound is proposed in this paper. Firstly, the external noise of traditional vehicle with internal combustion engine is collected, and by using speech synthesis technology to change the amplitudes of its sound signals in specific frequency band, a large number of sound signal samples are obtained. Then, based on the concept of sound quality, a subjective evaluation test is carried out, and the objective psychoacoustic parameters of each sound signal are calculated, from which the sound samples with the best score in the corresponding speed interval are selected as warning sounds. Finally, BP neural network is optimized by simulated annealing (SA) algorithm and genetic algorithm (GA), and based on subjective and objective evaluation data, a model objectively quantifying subjective evaluations of the quality of warning sounds for electric vehicle is established by SA/GA-BP neural network with 6 objective parameters as inputs and subjective evaluation value as output. The results show that the model set up has a fast convergence speed and a high prediction accuracy.