基于深度学习的发动机声品质预测模型研究OA北大核心CSTPCD

In order to develop a deep learning prediction model for the sound quality of engine radiated noise,a test bench was constructed to collect engine radiated noise.The psychological objective parameters of noise signals were determined,and subjective evaluation experiments were conducted.The psychological objective parameters of noise signals were determined,and subjective evaluation experiment was conducted.Signal features were extracted employing convolutional neural network(CNN),long-term dependence information was acquired utilizing long short-term memory(LSTM)network,and essential feature information was automatically learned with the Attention mechanism.The subjective evaluation score was applied as the output,while the psychological objective parameters were used as inputs.A sound quality prediction model for CNN-LSTM-Attention was developed.To increase prediction accuracy,the improved sparrow search algorithm(ISSA)was employed to adjust the hyperparameters of model.The results show that the ISSA-CNN-LSTM-Attention model accurately predicts engine sound quality.For the training and test sets,the average absolute errors are 0.204 and 0.241,respectively,while the coefficient of determination are 0.988 and 0.981,respectively.The model presents new perspectives and methods for predicting engine sound quality since it can effectively represent the nonlinear mapping relationship between objective parameters and subjective satisfaction.