Interior sound quality evaluation model of heavy commercial vehicles

Based on back-propagation (BP) artificial neural network (ANN) technique, interior sound quality evaluation model of heavy commercial vehicles(HCV) was designed in this paper. According to the standard named GB/T18697-2002, firstly, the interior noises of five different types of HCV under different working conditions were measured and collected. Secondly, the subjective evaluation of sound quality was estimated by jury tests following the magnitude estimation. Meanwhile, seven objective psycho-acoustical parameters of these samples were calculated. Using the objective and subjective evaluation results, furthermore, the sound quality prediction model of HCV was developed based on BP ANN. Thirdly, this model was proved by some verification tests. The results suggest that the proposed model has ability of high precision and good generalization. And lastly, the sound quality prediction model of HCV could be used to determine the impact weight of measuring objective evaluation parameters contributing to the results of subjective evaluation. The results played a significant guiding role in both HCV and other areas for sound quality evaluation and analysis

Interior sound quality evaluation model of heavy commercial vehicles

Based on back-propagation (BP) artificial neural network (ANN) technique, interior sound quality evaluation model of heavy commercial vehicles(HCV) was designed in this paper. According to the standard named GB/T18697-2002, firstly, the interior noises of five different types of HCV under different working conditions were measured and collected. Secondly, the subjective evaluation of sound quality was estimated by jury tests following the magnitude estimation. Meanwhile, seven objective psycho-acoustical parameters of these samples were calculated. Using the objective and subjective evaluation results, furthermore, the sound quality prediction model of HCV was developed based on BP ANN. Thirdly, this model was proved by some verification tests. The results suggest that the proposed model has ability of high precision and good generalization. And lastly, the sound quality prediction model of HCV could be used to determine the impact weight of measuring objective evaluation parameters contributing to the results of subjective evaluation. The results played a significant guiding role in both HCV and other areas for sound quality evaluation and analysis

Interior sound quality evaluation model of heavy commercial vehicles

Based on back-propagation (BP) artificial neural network (ANN) technique, interior sound quality evaluation model of heavy commercial vehicles(HCV) was designed in this paper. According to the standard named GB/T18697-2002, firstly, the interior noises of five different types of HCV under different working conditions were measured and collected. Secondly, the subjective evaluation of sound quality was estimated by jury tests following the magnitude estimation. Meanwhile, seven objective psycho-acoustical parameters of these samples were calculated. Using the objective and subjective evaluation results, furthermore, the sound quality prediction model of HCV was developed based on BP ANN. Thirdly, this model was proved by some verification tests. The results suggest that the proposed model has ability of high precision and good generalization. And lastly, the sound quality prediction model of HCV could be used to determine the impact weight of measuring objective evaluation parameters contributing to the results of subjective evaluation. The results played a significant guiding role in both HCV and other areas for sound quality evaluation and analysis

Interior sound quality evaluation model of heavy commercial vehicles

Based on back-propagation (BP) artificial neural network (ANN) technique, interior sound quality evaluation model of heavy commercial vehicles(HCV) was designed in this paper. According to the standard named GB/T18697-2002, firstly, the interior noises of five different types of HCV under different working conditions were measured and collected. Secondly, the subjective evaluation of sound quality was estimated by jury tests following the magnitude estimation. Meanwhile, seven objective psycho-acoustical parameters of these samples were calculated. Using the objective and subjective evaluation results, furthermore, the sound quality prediction model of HCV was developed based on BP ANN. Thirdly, this model was proved by some verification tests. The results suggest that the proposed model has ability of high precision and good generalization. And lastly, the sound quality prediction model of HCV could be used to determine the impact weight of measuring objective evaluation parameters contributing to the results of subjective evaluation. The results played a significant guiding role in both HCV and other areas for sound quality evaluation and analysis