Abstract
To improve the accuracy and efficiency of the objective evaluation of noise quality from internal combustion engines, an automatic noise quality classification model was constructed by introducing an auditory model-based acoustic spectrum analysis method and a convolutional neural network (CNN) model. A band-pass filter was also designed in the model to automatically extract the features of the noise samples, which were later used as input data. The adaptive moment estimation (Adam) algorithm was used to optimize the weights of each layer in the network, and the model was used to evaluate sound quality. To evaluate the predictive performance of the CNN model based on the auditory input, a back propagation (BP) sound quality evaluation model based on psychoacoustic parameters was constructed and used as a control. When processing the label values of the samples, the correlation between the psychoacoustic parameters of the objective evaluation and evaluation scores was analyzed. Four psychoacoustic parameters with the greatest correlation with subjective evaluation results were selected as the input values of the BP model. The results showed that the sound quality evaluation model based on the CNN could predict the sound quality of internal combustion engines more accurately, and the input evaluation score based on the auditory spectrum in the CNN classification model was more accurate than the short-time average energy input evaluation score based on the time domain.
Highlights
To improve the accuracy and efficiency of the objective evaluation of noise quality from internal combustion engines, an automatic noise quality classification model was constructed by introducing an auditory model-based acoustic spectrum analysis method and a convolutional neural network (CNN) model
To evaluate the predictive performance of the CNN model based on the auditory input, a back propagation (BP) sound quality evaluation model based on psychoacoustic parameters was constructed and used as a control
Four psychoacoustic parameters with the greatest correlation with subjective evaluation results were selected as the input values of the BP model. e results showed that the sound quality evaluation model based on the CNN could predict the sound quality of internal combustion engines more accurately, and the input evaluation score based on the auditory spectrum in the CNN classification model was more accurate than the short-time average energy input evaluation score based on the time domain
Summary
The feature points in this frame were arranged in ascending order. e vertical coordinates of the input data in the gure are frequency bands. vl−1 denotes eigenvalue vl−1 of the l − 1th layer, and vb denotes the eigenvalue o[vf 1th, ve2b,t.h.b.a,nvdb]. It vlj of the convolution layer was calculated as follows:. A convolution calculation was performed on all feature maps vli−1 of layer l − 1 and the jth convolution kernel klj of layer l. E right side was obtained by adding the sum and the o set bj of the jth feature map It was calculated using an activation function f(x), which was a recti ed linear unit (ReLU) function [10], shown as follows: f(x) max(0, x). E low-resolution representation of the feature map obtained by the convolution layer was calculated using a downsampling method. A maxpooling function is often used to calculate the maximum value of a feature map obtained by convolution (continuous frequency band), and its formula is as follows: pj,m Max vj,(m−1)×n+k , k 1, . If the sampling factor n was used in the downsampling, the upsampling operation in the back propagation enlarged each feature map by n times in the horizontal and vertical dimensions. erefore, the Kronecker product [13] was used to complete the calculation
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