Research on the Sound Quality Evaluation Method Based on Artificial Neural Network

Abstract

For the improvement of the traditional evaluation effect of the automobile sound quality, an evaluation model of automobile sound quality is constructed based on BP neural network. The first is to introduce the basic principle of the BP neural network in detail. The second is to use the MGC parameters to establish the vehicle interior sound conversion model. The converted sound characteristic parameters are taken into the WORLD model to synthesize the new sound signals. Furthermore, the wavelet decomposition method is used to remove noise from the synthesized sound signals. Finally, a sound evaluation model based on BP neural network is established. The sound quality of automobiles can be better evaluated by carrying out the ABX test and MOS test in the field of sound conversion. For the newly synthesized sound signal and the target sound signal, it can be seen that the newly synthesized sound signal is more inclined to the target sound signal, and the sound quality is better. In addition, the sound quality is tested through loudness, roughness, sharpness, and level A in the field of sound quality evaluation. The final results show that the quality of newly synthesized sound is better, and the average errors of sound signals meet the sound standard. Therefore, the constructed sound conversion model and the sound evaluation model are feasible and effective.

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

“…In the field of sound quality research, many researchers use loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI), and other parameters as objective evaluation. After the subjective evaluation score test, an appropriate regression model and method are selected to fit the nonlinear relationship between the parameters and human ear perception [5,6]. In the early stage, the method of multiple linear regression was used to predict the sound quality, but there is often serious multicollinearity among the parameters, resulting in poor accuracy of the model.…”

Research on active sound control in EV in-vehicle based on engine order sound fitting

“…A new model between objective acoustic parameters and subjective evaluation was constructed by the relevant software. Song and Yang 10 constructed a model to evaluate the sound quality of the car by means of BP (Back Propagation) neural network. Sharpness, loudness, and roughness were used as reference indicators for evaluating sound quality.…”

Fuzzy Adaptive PSO-ELM Algorithm Applied to Vehicle Sound Quality Prediction

“…Many scholars use different neural network methods to predict sound quality, including back propagation neural network (BPNN) [13,14], deep convolutional neural networks (CNNs) [15], radial basis function (RBF) [16], genetic algorithm [17], and others [18][19][20][21]. Hou [6] built the standard BPNN model, the genetic algorithm back propagation neural network (GA-BPNN) model and back propagation neural network based on particle swarm optimization (PSO) and proved that the PSO-BPNN model can achieve convergence more quickly and improve the prediction accuracy of sound quality.…”