In order to solve the problem of low efficiency and high cost in the evaluation of sound quality of internal combustion engines

the convolutional neural network(CNN) model and sound spectrum analysis method were used to construct a CNN sound quality prediction model(CNNSQP model) to evaluate the sound quality. Noise samples were extracted automatically by the Gammatone filter and used as input data. The adaptive moment estimation(Adam) algorithm was used to optimize the weights of the layers in the network. To prove the predictive performance of the CNNSQP model

a back propagation(BP) sound quality evaluation model based on psychoacoustics objective parameters was built and used for comparison experiments. The sample label value(manual evaluation) was processed

and four psychoacoustics objective parameters that are most related to the manual evaluation result were selected as the input values of the BP model for prediction. The experimental results show that the CNN-based sound quality assessment model predicts the sound quality of internal combustion engines more accurately than the assessment based on the BP model

and the input evaluation value based on the auditory spectrum in the CNNSQP model is more precise than that based on short-term average energy in time domain and spectral flux in frequency domain.