Sound quality prediction for engine-radiated noise

Abstract Diesel engine-radiated noise quality prediction is an important topic because engine noise has a significant impact on the overall vehicle noise. Sound quality prediction is based on subjective and objective evaluation of engine noise. The integrated satisfaction index (ISI) is proposed as a criterion for differentiate noise quality in the subjective evaluation, and five psychoacoustic parameters are selected for characterizing and analyzing the noise quality of the diesel engine objectively. The combination of support vector machines (SVM) and genetic algorithm (GA) is proposed in order to establish a model for predicting the diesel engine-radiated noise quality for all operation conditions. The performance of the GA-SVM model is compared with the BP neural network model, and the results show that the mean relative error of the GA-SVM model is smaller than the BP neural network model. The importance rank of the sound quality metrics to the ISI is indicated by the non-parametric correlation analysis. This study suggests that the GA-SVM model is very useful for accurately predicting the diesel engine-radiated noise quality.

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