Psychoacoustic evaluation of a garden tractor noise

In addition to achieving sustainable development of agricultural mechanization, it has causing problems on occupational health and safety for people working in different fields of agriculture. Noise is considered as one of the most debilitating conditions in farming and a comprehensive investigation of this relationship is required. In this study, some factors affecting the noise generated by a Goldoni garden tractor were evaluated. Research factors were including engine speed, gear ratios and type of operation. Accordingly, factorial experiments in completely randomized design with three replicates were performed. According to variance analysis with LAeq, PA and UBA, operation type, gear ratio and engine speed were found to be significant (P< 0.01). The results of this study indicate that PA and UBA correlated strongly with LAeq analysis (R2=0.97). The results also show that LAeq, PA and UBA for rural road are higher than tillage condition. Also, results indicated that the highest mean of LAeq, PA and UBA were 77.76 dBA, 9.83 and 21.16, respectively and occurred in the case of rural road and 2100 rpm engine speed

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

Towards improving ViSQOL (Virtual Speech Quality Objective Listener) Using Machine Learning Techniques

Vast amounts of sound data are transmitted every second over digital networks. VoIP services and cellular networks transmit speech data in increasingly greater volumes. Objective sound quality models provide an essential function to measure the quality of this data in real-time. However, these models can suffer from a lack of accuracy with various degradations over networks. This research uses machine learning techniques to create one support vector regression and three neural network mapping models for use with ViSQOLAudio. Each of the mapping models (including ViSQOL and ViSQOLAudio) are tested against two separate speech datasets in order to comparatively study accuracy results. Despite the slight cost in positive linear correlation and slight increase in error rate, the study finds that a neural network mapping model with ViSQOLAudio provides the highest levels of accuracy in objective speech quality measurement. In some cases, the accuracy levels can be over double that of ViSQOL. The research demonstrates that ViSQOLAudio can be altered to provide an objective speech quality metric greater than that of ViSQOL