Interior Sound Quality Research Articles

Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.

Sound quality evaluation in special vehicles based on multiple linear regression

The issue of interior sound quality in specialized vehicles has received widespread attention, which could directly affect the riding comfort of the vehicle. This research primarily focuses on the objective quantification of subjective perception of sound qualitySQ in special vehicles. The noise of special vehicles under different working conditions is measured and analyzed, and psychoacoustic parameters and A-weighted sound pressure levels are calculated. The subjective evaluation of interior sound quality was conducted using the grading scale method. A multiple linear regression model is used to establish the correspondence between subjective perceptions and objective parameters. It provides a reference basis for evaluating the sound quality of special vehicles.

Interior sound quality evaluation and forecasting of passenger vehicles based on hybrid optimization neural networks

The interior noise of vehicles directly affects the comfort of the occupants, necessitating precise evaluation and control. Existing research has focused on constructing mappings between objective parameters and subjective perceptions of noise, where back propagation neural networks (BPNNs) are widely used due to their strong nonlinear mapping capabilities. However, the selection of initial weights and thresholds can affect the predictive accuracy of BPNN. This study developed a BPNN model optimized by an intelligent algorithm for predicting the level of subjective annoyance of passengers during the movement. Initially, objective parameters of interior noise were obtained through acoustic signal processing techniques, and five parameters were selected for studying subjective annoyance through correlation analysis and two-tailed tests. Meanwhile, the actual subjective ratings of passengers on interior noise were captured for subsequent training of the model and testing of the results. Finally, the established sparrow search algorithm (SSA) and genetic algorithm (GA) optimized BPNN were used to predict subjective evaluations. The predictive accuracy and efficiency of the model were significantly improved upon validation, providing a viable alternative to traditional passenger vehicle noise assessment experiments and valuable references for future noise control and optimization efforts. The experimental results are consistent with the view that the neural network model optimized with a mixture of intelligent algorithms is closer to the passenger’s subjective annoyance level having higher accuracy and efficiency.

Research on the characteristics of EV interior sound quality and its dynamic active control system design and development un-der accelerated driving conditions.

In order to improve the interior sound quality of Electric Vehicles (EV), solve the problem of low sense of power and comfort of the interior sound as well as the large electromagnetic excitation order noise of motor and the sharp interior sound, this article designs a dynamic active sound control system for EV under accelerated driving conditions. Firstly, by comparing and analyzing the sound spectrum characteristics of fuel vehicle (FV) and EV during acceleration, a short-time Fourier transform (STFT) is adopted to extract and synthesize the engine sound. Secondly, the influence of the engine order composition and the energy distribution in the frequency domain on the sound quality of the vehicle is analyzed, and an active control system for sound quality is proposed. And the software and hardware development of the active control sound system is completed. Finally, through real-vehicle testing and verification, the sense of comfort and power of the EV interior sound has been greatly improved during acceleration, and the total value of interior sound can meet the requirement. The sound pressure level and loudness of interior sound have been increased, and the sharpness of the sound inside the vehicle has been improved, with a maximum reduction of 1.0acum.

Research on the parameter design and calibration and control of EV in-vehicle active sound generation system

In order to improve the interior sound quality of electric vehicles (EVs) under acceleration and uniform speed conditions, to balance the comfort and dynamics of the interior sound, and to improve the accuracy and performance of the active sound generation system (ASGS), this article carries out the research related to the parameter design, sound calibration, evaluation methodology, and control system of the EV ASGS. Propose an in-vehicle sound design method focusing on three dimensions, including engine order composition, spectral energy distribution, and sound amplitude enhancement in the typical speed range, and determine the in-vehicle sound design scheme and the total sound value target. Focus on the sound parameter design, calibration and evaluation methods of EV ASGS considering the frequency response characteristics of the loudspeaker, sound amplitude control accuracy, sound quality, and psychoacoustic parameters, clarify the active sound parameter settings of EVs, complete the analysis of sound extraction methods, complete the engine order sound fitting, and design the ASGS of the EV interior by combining the subjective and objective evaluations. Develop the control software and hardware of the ASGS, complete the construction and accuracy verification of the ASGS based on the in-vehicle sound system, and realize the sound calibration of the ASGS under the static conditions of the real vehicle and the verification of the target achievement. The real-vehicle test shows that the ASGS reduces the sharpness of 1.0 acum and 0.52 acum under acceleration and constant speed conditions, respectively, and improves the comfort and dynamics of in-vehicle sound. The objective and subjective evaluation results show that the parameter design, selection and accuracy of the sound calibration and evaluation methods of the ASGS in the EV determines the accuracy and effect of the ASGS.

A hybrid simulation approach for frictional vibration and noise of wiper-windshield system based on rigid-flexible and acoustic-structure coupling models

Automotive wiper-windshield friction noise is one of the important factors affecting the vehicle interior sound quality. Due to its transient, nonlinear, and uncertain characteristics, the wiper-windshield friction noise has always been a problem in vehicle engineering. Based on rigid-flexible and acoustic-structure coupling models, a hybrid simulation procedure for the friction-induced vibration and noise of a wiper-windshield system is presented in this paper. In light of the rigid-flexible coupled dynamic equations and acoustic-structure coupled automatic matched layer (AML) based on the finite element method (FEM), the software ANSYS Workbench and LMS Virtual. lab. is selected and used for modeling and simulating the frictional vibration and noise of the system under different working speeds and lubricating conditions. In the friction noise simulations, the calculated vibrational responses from the ANSYS are input into the LMS as the excitations of a virtual sound field in a vehicle cavity. Experimental verifications suggest that the proposed hybrid approach is effective and feasible for frictional vibration and noise simulation of the wiper-windshield system. The work done in this paper may provide theoretical and technical supports for acoustic design of wiper-windshield systems, and has guiding significance for interior sound-quality optimization and control of vehicles.

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

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.

Strategies for Reducing Booming Noise Generated by the Tailgate of an Electric Sport Utility Vehicle

This article investigates the source of booming noise emanating from the tailgate of an electric sport utility vehicle (SUV), along with proposed strategies to mitigate it. This annoying low-frequency booming noise, which significantly impacts interior sound quality, is less perceptible in conventional internal combustion engine vehicles. However, this noise is more readily detected in electric SUVs, highlighting the necessity for focused measures to reduce it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, ODSs were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes, resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite-element analysis software to propose measures for reducing booming noise. Experimental findings validated this model’s accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to reduce the booming noise resulting from tailgate motion. Under random force inputs, the analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness. This study identifies the mechanism generating booming noise, establishes a practical and simple dynamic model, and proposes improvement measures aimed at reducing the booming noise.

Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO

There is no doubt that traffic noise has become one of the main sources of urban noise, and the electric bus, as an important means of transport frequently used by people in daily life, has a direct impact on the psychological and auditory health of passengers due to its interior noise characteristics. Consequently, studying electric bus sound quality is an important way to improve vehicle performance and comfort. In this paper, eight electric buses were selected and 64 noise samples were measured. Acoustic comfort was taken as an evaluation index, professionals were organized to complete the subjective evaluation tests for all noise samples based on rank score comparison (RSC). And nine psycho-acoustic objective parameters such as loudness, sharpness and roughness were calculated using ArtemiS software to establish the sound quality database of electric buses. Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost and PSO-XGBoost through data training. Finally, the calculation results of three indexes of average relative error, square root error and correlation coefficient indicate that the proposed PSO-XGBoost model is significantly better than GS-XGBoost and standard XGBoost, with its prediction accuracy as high as 97.6 %. This model is determined as the evaluation model of interior acoustic comfort for this case, providing a key technical support for future sound quality optimization of electric buses.

Research on evaluation model for vehicle interior sound quality based on an optimized BiLSTM using genetic algorithm

Interior sound quality strongly affects passengers’ physiological and psychological perceptions. Therefore, it is important to evaluate vehicle interior sound quality. Compared with assessing by humans, the artificial intelligent-based evaluation model can acquire an evaluation efficiently. However, this type of models determines initial learnable parameters at random before training, which is easy to cause final trained model to trap in local optima. This paper proposes an evaluation model based on an optimized bidirectional long short-term memory using genetic algorithm. Firstly, interior noise measurement and subjective evaluation are completed. Secondly, to obtain the time–frequency information in line with human auditory perception, log-mel spectrum is used to preprocess the noise. Thirdly, the evaluation model is constructed, which consists of two bidirectional long short-term memory layers, two fully connected layers and one Softmax output unit. Next, to avoid model trapping in local optima, initial learnable parameters are optimized using genetic algorithm. After optimization, average fitness and best fitness decreased by 6.5136% and 1.4415%, respectively. The training accuracy is 95.79%. The validation accuracy is 93.15%. The testing accuracy is 93.33%. Only two samples are misclassified in the confusion matrix of testing stage. These suggest that genetic algorithm can greatly enhance the model's performance by optimizing initial learnable parameters. The evaluation obtained by the optimized model is very close to human subjective evaluation.

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

The research on vehicle sound quality is an emerging and highly promising field. This paper proposes a solution for sound quality improvement based on sensitivity analysis of psycho-acoustic parameters, in which the main acoustic techniques involved include noise signal acquisition, subjective and objective evaluations, sound quality modelling and prediction, parameter sensitivity and spectral analysis, and fuzzy comprehensive evaluation. In addition, taking 64 noise samples of electric bus as an application case, the presented sound quality analysis steps are implemented successively, and the obtained test results of improving the whole vehicle sound quality verify the feasibility and effectiveness of the proposed scheme.

Active Control Algorithm of Hybrid Vibroacoustic for Vehicle Interior Sound Quality

Background: In some applications, retaining the residual noise at a specified frequency can provide a better vehicle interior sound quality for occupant’s driving experience. The hybrid vibro-acoustic adaptive noise equalizer (HVA-ANE) algorithm introduces a sound retention factor on the basis of the hybrid vibro-acoustic filter-u, and filter-x least mean square (HVA-FUXLMS) algorithm to achieve control of the sound quality, but it is not possible to retain varying degrees of different frequency bands. The hybrid vibro-acoustic empirical-mode-decomposition frequencydomain block adaptive noise equalizer (HVA-EBANE) algorithm introduces Empirical-Mode- Decomposition (EMD) and frequency domain block methods to achieve different degrees of retention of noise in different frequency bands but cannot deal with the irrelevant frequency components in the error signal. Objective: This paper proposes an improved active control algorithm for vehicle interior sound quality, the so-called hybrid vibro-acoustic EMD frequency-domain block FELMS (HVA-EBFELMS) algorithm. Methods: Based on the filtered error, the least mean square algorithm and the residual filter of the specific frequency band are designed according to the psychoacoustic parameters of each component. The filter filters the irrelevant frequency components in the residual signal, and only retains the frequency components that have a great influence on the sound quality so as to achieve the regulation of the desired sound quality. Results: In order to verify the effectiveness of our algorithm, we used the noise and vibration in the vehicle at idle speed as the object and compared the noise control effect with HVA-ANE and HVAEBANE algorithms considering the sound quality. The simulation experiment shows that our algorithm reduces the loudness of the ear-side noise by more than 40% while taking into account the noise and vibration control. Conclusion: The loudness control effect of hybrid vibro-acoustic EMD frequency-domain block FELMS (HVA-EBFELMS) algorithm is better than HVA-ANE and HVA-EBANE algorithms; it has a better effect of improving loudness as well.

Development of an Active Sound Generation System for Sound Quality in EVs at Constant Speed Combined with Subjective Evaluation

In order to improve the comfort and dynamic sense of the interior sound of PEVs when driving at a constant speed, this paper carried out research on the evaluation method of the interior sound quality of EVs and the development of an active sound generation system (ASGS). The investigation and research on the sound development trend of EV interiors were carried out. Combined with subjective evaluation methods and test results, a constant-speed vehicle interior sound design method was proposed, which focused on three dimensions: engine order composition, spectral energy distribution, and sound amplitude enhancement in the typical vehicle speed range. The internal sound spectrum of FVs was analyzed, and the engine order sound simulation analysis was completed. It is verified that the short-time Fourier transform and synthesis technology based on Kaiser window function can meet the requirements of engine order sound fitting accuracy. To formulate the sound design plan, control objectives and control methods of an A-class pure electric SUV with both comfort and dynamic sense during accelerated driving, the correlation between the engine order sound amplitude and dynamic sense in the vehicle was analyzed, and the setting of active sound parameters of EVs was clarified. The control hardware and software of ASGS in the vehicle was developed, the construction of ASGS based on the vehicle sound system completed, and test verification was conducted. The objective test of the real vehicle shows that ASGS ensures a low-amplitude interior noise level. When the vehicle speed is 28.6 km/h, the sharpness is reduced by 0.52 acum, and the subjective evaluation test of the real vehicle further verifies that ASGS can improve the comfort and dynamic sound quality of EVs driving at a constant speed.

Nonlinear overall annoyance level modeling and interior sound quality prediction for pure electric vehicle with extreme gradient boosting algorithm

A single psychoacoustic metric cannot reflect pure electric vehicle (PEV) interior overall sound quality (SQ) accurately. There exists a complex nonlinear mapping relationship between psychoacoustic indexes and SQ prediction. In this work, a nonlinear overall annoyance level modeling method using the extreme gradient boosting (XGBoost) algorithm is proposed. Firstly, comprehensive noise samples inside the cabins of PEV and conventional fuel vehicle (FV) at high speed are collected. Subjective and objective SQ evaluations are conducted for FV and PEV interior noise, respectively. Their differences are analyzed by comparison. The correlation among various SQ indexes is studied, revealing a significant multicollinearity disruption. Then, the relative importance ranking is determined by the random forests algorithm, which provides a basis for feature index selection. Finally, the XGBoost-based nonlinear overall annoyance level model for SQ evaluation is established. Using the XGBoost-based model, traditional multiple linear regression (MLR) model, and linear least absolute shrinkage and selection operator (LASSO) model, PEV SQ is predicted. The results show that the XGBoost-based model exhibits high prediction accuracy, improving 75.6% relative to the MLR model and 74.9% relative to the LASSO model. Meanwhile, it has excellent stability and goodness-of-fitting. Thus, it provides an alternative method for PEV SQ evaluation and prediction.

Hybrid vibro-acoustic active control method for vehicle interior sound quality under high-speed

For the problem of active noise control (ANC) and active vibration control (AVC) methods, only noise and vibration can be controlled separately. Active sound quality control (ASQC) method and AVC method are combined to carry out an in-depth study on the active control method of vehicle interior sound quality, using the vibration-sound transfer function and considering the requirements of sound quality. Combined with the adaptive noise equalizer (ANE) algorithm, a sound retention factor was introduced on the basis of the hybrid vibro-acoustic filter-u and filter-x least mean square (HVA-FUXLMS) algorithm, a hybrid vibro-acoustic adaptive noise equalizer (HVA-ANE) algorithm was proposed to control the sound quality. Based on the filter-error least mean square algorithm (FELMS) algorithm, a hybrid vibro-acoustic empirical-mode-decomposition frequency-domain block filter-error least mean square (HVA-EMDFELMS) algorithm for vehicle interior sound quality was proposed. Vibration and noise at high speeds are collected and analyzed for sound quality levels. This article used HVA-ANE and HVA-EMDFELMS algorithms to perform ASQC simulation experiments. The results showed that the noise loudness was greatly reduced while the noise and vibration control was taken into account by the HVA-ANE algorithm and HVA-EMDFELMS algorithm. Hybrid vibro-acoustic active control method for vehicle interior sound quality can effectively improve the sound quality of vehicle and can be applied to the active control of vehicle noise and related engineering noise.

A review of commercial vehicle cab NVH research

With the development of social economy, people’s demands for vehicle functions are constantly changing. The comfort, intelligence, electric and energy saving of vehicles have gradually become the core technology of modern vehicle design. NVH (Noise Vibration Harness, NVH) performance, as the core index of driving comfort, is gradually paid attention to by automobile design manufacturers and users. How to reduce the interior noise and improve the interior sound quality has always been the focus of research in the field of vehicle NVH. This paper summarizes the research status of commercial vehicle NVH.

Strategy and implementing techniques for the sound quality target of car interior noise during acceleration

The subjective impression of car driving is affected mainly by noise, so it is necessary to develop the car interior sound quality with different characteristics according to the market positioning of the vehicle. “Powerfulness” is a kind of sound quality characteristic during acceleration. However, its subjective evaluation method and key influence factors are still not precise and cannot effectively guide the car interior sound quality design. In this paper, the subjective evaluation method of powerful sound quality and the strategy of the car interior noise target during acceleration are studied. Firstly, the Factor Analysis Method is used to carry out Dimension Reduction Analysis on 50 pairs of antisense words describing “Powerfulness”. The result shows that powerful sound quality can be evaluated from three orthogonal dimensions: “Trilling”, “Richness”, and “Smoothness”. Secondly, in view of the disadvantages of normal semantic differential method that it is difficult to evaluate the accelerating noise with time-varying non-steady-state features accurately, the Continuous Assessment by Predefined Categories (CAPC) subjective evaluation method is applied to the subjective evaluation of 24 car interior sound samples with different sound quality characteristics during acceleration. The objective parameters of the sound samples is calculated and analyzed for their correlation with the subjective evaluation results. The results show that the three subjective evaluation dimensions are related to the frequency components, order ratio, and linearity of the sound samples, respectively. Thirdly, a development process for the target of powerful sound quality is presented, including writing sound modulation software and artificially synthesizing sound samples with different parametric characteristics. Through the audition and evaluation of synthesized sound samples, two powerful sound quality features, namely Order Relay and Frequency Retention, are proposed by statistical methods. The support vector machine model verifies the rationality of the proposed features. At last, the case of realizing the car interior powerful sound quality by tuning the intake system is discussed. The research in this paper can provide a reference for the sound quality design during acceleration in passenger cars.

Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

전기자동차의 사운드디자인 개선을 위한 CFRP 스피커의 음향 특성 연구

The noise characteristics of electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) are different. Compared with vehicles with combustion engines, cars are quieter and have a lower interior overall sound pressure. The sound quality in the compartment of an EV degrades because of high frequency motor noise and road noise, which are not masked by engine noise. Therefore, to improve sound quality, an EV requires active sound generation (ASG) systems that can actively control interior sound quality. ASG requests an audio speaker with superior high frequency response to mask or control the level of high frequency noise. This study aims to develop carbon fiber reinforced plastic (CFRP) speakers with superior high frequency response. Using a commercial CFRP speaker, a numerical model for the proposed speaker was developed via a finite element (FE) method and experimentally validated. The frequency response of a CFRP speaker and general car audio speaker was analyzed using the developed FE model, and the sound radiation characteristics of two speakers were compared. The CFRP speaker was confirmed to have a superior high frequency response to a general audio speaker.

Research of transfer path analysis based on contribution factor of sound quality

Transfer Path Analysis (TPA) can accurately and effectively determine the composition of the structural vibration transfer path and its impact on the interior noise. To further explore the effect of structural paths on the sound quality in the car, a GA-BP sound quality prediction model was established. By introducing the Sound Quality Contribution Factor (SQCF), the contribution of structure noise transmission path to irritability and the influence of masking effect on sound quality was more intuitively reflected. Aiming at the contribution, a two-level optimization scheme is adopted, and a genetic algorithm is used to determine the target transfer function corresponding to the target irritability value, and then the target path suspension static rigidity is further matched. The results show that the engine suspension optimization scheme based on the sound quality contribution factor can effectively improve the interior sound quality and reduce the contribution of the structural path to the irritability.