A case study on improving electric bus interior sound quality based on sensitivity analysis of psycho-acoustics parameters

Zhang, Enlai; Zhang, Peng; Zhuo, Jianming

doi:10.1177/09574565231193050

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, an electric bus was taken as the application target, with reference to test and evaluation methods of city bus internal noise (XMQT075-2021), the driving position was selected as a signal observation point, and the electric bus traveled steadily at uniform speeds of 30 km/h and 50 km/h on the professional test runway. 20,21 Noise signals were collected with the Squadriga II binaural acquisition system and head-mounted BHS II of HAED Acoustics, and measurement point distribution, test scene and instruments are shown in Figures 5 and 6, respectively. It needs to be added that the BHS II built-in small microphone, combined with the Squadriga II system, can obtain the transient sound pressure signals near the driver’s ear in real time, and perform high-fidelity playback of noise signals.…”

Section: Anc Modeling For An Electric Bus Driving Positionmentioning

confidence: 99%

Active noise control modeling for an electric bus driving position and its effect on sound quality

Zhang,

Peng,

Chen

et al. 2024

Noise & Vibration Worldwide

Self Cite

View full text Add to dashboard Cite

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver’s physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver’s ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality.

show abstract

Section: Anc Modeling For An Electric Bus Driving Positionmentioning

confidence: 99%

Active noise control modeling for an electric bus driving position and its effect on sound quality

Zhang,

Peng,

Chen

et al. 2024

Noise & Vibration Worldwide

Self Cite

View full text Add to dashboard Cite

show abstract

Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library

Zhang,

Mui,

Masullo

et al. 2024

Acoustics

View full text Add to dashboard Cite

Understanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. However, predictive models are not specifically tailored to predict students’ acoustic evaluations, particularly in educational settings. To bridge this gap, the present study conducted a field investigation in a university library, including a measurement and questionnaire survey. Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. The performance of these models was evaluated using five metrics, allowing for a comparative analysis. The results revealed that the models better predicted acoustic acceptance than acoustic satisfaction. Notably, the RF and GBM models exhibited the highest performance, with accuracies of 0.87 and 0.84, respectively, in predicting acoustic acceptance. Conversely, the SVM models performed poorly and were not recommended for acoustic quality prediction. The findings of this study demonstrated the feasibility of employing machine learning models to predict occupants’ acoustic evaluations, thereby providing valuable insights for future acoustic assessments.

show abstract

A case study on improving electric bus interior sound quality based on sensitivity analysis of psycho-acoustics parameters

Cited by 2 publications

References 19 publications

Active noise control modeling for an electric bus driving position and its effect on sound quality

Active noise control modeling for an electric bus driving position and its effect on sound quality

Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library

Contact Info

Product

Resources

About