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| Research on Engine Sound Quality Prediction Model Based on Deep Learning |
| DOI：10.13949/j.cnki.nrjgc.XXXX.XX.001 |
| Key Words:engine sound quality prediction model improved sparrow search algorithm (ISSA) |
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| 摘要点击次数: 74 |
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| Abstract:A test bench was constructed to collect engine radiated noise in order to develop a deep learning prediction model for the sound quality of 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 are extracted employing Convolutional Neural Network (CNN), long-term dependence information is acquired utilizing Long Short-Term Memory Network (LSTM), and essential feature information is automatically learned with the Attention mechanism (Attention). 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 is 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. This 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. |
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