车内噪声声品质建模分析与自适应主动控制研究
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摘要
本文是结合教育部“优秀青年教师资助计划”专项科研基金资助项目《车内非稳态噪声自适应主动控制的神经网络方法》以及吉林省科技发展计划重点项目《轿车车内风噪声和轰鸣声的分析与控制》展开研究的。论文建立了以心理声学客观参量描述车内声品质的数学模型，提出了声品质主动控制算法和相应控制策略，实际构建了双通道车内声品质自适应主动控制系统，并利用该系统在某型轿车上进行了试验验证。论文的主要工作和研究结论有：
开发了可计算响度、尖锐度、粗糙度和抖动度四个心理声学参量的声品质客观评价软件，并验证了所编程序的计算可靠度和准确度。
以等级评分方法对采自8种不同类型国产轿车的32个稳态车内声音样本进行了主观评价试验，再以回归分析方法，建立了以心理声学客观参量响度和尖锐度描述的声品质烦躁度数学模型。
提出了可实现车内声品质改善的控制方法和相应控制策略：通过引入增益系数对不同频段噪声实现程度不同的可控性抵消；以二级自适应滤波器降低随机成分干扰；并按照声品质指标最优化原则，确定适宜的增益系数向量。仿真建模分析证明，该方法及相应控制策略是有效的。
以DSP功能板为硬件核心，研制了自适应主动控制器，并开发了相应软件，在某国产轿车上实际构建了车内声品质自适应主动控制系统。
利用该系统对车内副驾驶员两耳旁噪声实施了主动控制试验，在汽车匀速行驶的四种不同稳态工况下，所建系统都能够有效降低车内噪声的响度和尖锐度，特别是响度的下降更为显著，相应的车内声品质烦躁度等级也下降了25％以上。
Consumers are often deeply affected by their subjective impression of vehicle interior noise when choosing a car, while A weight sound pressure level, as one evaluating indicator in the traditional research on automotive noise, can't fully show the annoyance grade of a driver or passenger to the vehicle interior noise. So the concept Sound Quality to reflect personal subjective feeling of noise arises, and has been one of the important indexes used to evaluate all kinds of sound. And the research on evaluation, analysis and control of sound quality has come to more and more attention.
The key to improve the sound quality of vehicle interior noise lies in reducing the specific noise components to put listeners to annoyance. In this aspect, active noise control (ANC) technique, based on the principle of superposition whereby an unwanted primary noise can be canceled by the additional secondary noise of equal amplitude and opposite phase, has a good application prospect because of the obvious effect on low-frequency noise, the strong especial purpose and the selective noise elimination.
The evaluation and control for the sound quality of vehicle interior noise was studied, and improving sound quality of the interior noise in a car with ANC method was attempted in this paper. First, an equation between the subjective evaluation and the objective psycho-acoustical parameters was gained. Then an active sound-quality control (ASQC) adaptive algorithm and the relevant control strategy were brought forward. After the ASQC system of binaural channels had been established, the active control experiments were conducted in a car at last. The main works completed in the paper are as follows:
The sound quality of automobile product can be described with psycho-acoustical objective parameters as lots of foreign research indicates, so the principle of choosing proper psycho-acoustical parameters suitable to interior noise characteristic was given, and by this means four parameters loudness, sharpness, roughness and fluctuation were selected for the subsequent analysis. Then a set of software to assess sound quality objectively with the aforementioned four parameters was developed by our group according to ISO532B and other common models. A group of steady noise signals from an automotive cabin were calculated with this software and ArtemiS, one of sound-quality software developed by German company Head Acoustics, and the contrast between the two results of four parameters shows a small difference. It is obvious that our software can be used to analysis and appraise the sound quality of automobile because of enough credibility and accuracy.
The 32 steady samples of vehicle interior noise near the ears of assistant driver from 8 types of cars at different gears and velocities in a flat asphalt road were selected as evaluating objects, and the subjective evaluation tests were carried out with the magnitude estimation method by a jury composed of 30 appraisers. After the evaluating data strictly verified, there are 6 persons' results were not accepted due to the significant discrepancy with others, and the left 24 ones' evaluating scores were averaged as annoyance grade of every sample. Then by means of correlation and regression analysis between the subjective appraisal results and four psycho-acoustical objective parameters of 32 sound samples, a sound-quality annoyance model with which the subjective evaluation be quantitatively expressed with objective parameters was established. The model shows that sound quality of the interior noise mainly depends on loudness and sharpness, especially the former, under the stable conditions that vehicles running at various uniform speeds. And it gives a direction for how to improve sound quality.
On the basis of the above-mentioned work, theoretical research and simulation analysis on an ANC system, its algorithm and the relevant control strategy for improving sound quality were done. At first, according to the close correlation between dominant noise components and vibrating acceleration signals of engine-body and auto-body suspension, the reference signals of interior noise were distinguished and predicted by Elman dynamic neural network (NN) that the vibrating acceleration signals were used as its input, and after operational factors of the Elman NN had been chose through the simulation compare, the acoustic feedback disturbance of feed-forward ANC system was successfully avoided.
The next work was finishing an active sound-quality control algorithm of the vehicle interior noise with the active noise equalization (ANE) and filtered-error least mean square (FELMS) technique as its core. The ASQC algorithm can make up the insufficiency of the traditional ANC system that the noise cancelled degree can not be controlled depending upon different frequency bands and be easily disturbed by random sound components, as a result of two prominent characteristics: First, gain factor, one new parameter, is applied to control the acoustic attenuation, and the noise of different frequency will be active cancelled to different degrees, if they have respective different gain factors. Second, passband disturbance caused by some random noise components uncorrelated to reference signals can be reduced through two-layer adaptive filters. Simulations under various parameters show remarkable performance of this ASQC algorithm.
After convergent rate of the ASQC algorithm and computing speed of psycho-acoustical parameters had been considered together, the active sound-quality control strategy of the vehicle interior noise was put forward: The primary noise was divided into some simple harmonic waves with different frequencies and amplitudes after being digitally filtered and frequency-divided, and these simple harmonic signals were active controlled at the respective gain factors corresponding to different critical bands. At the same time, the aforementioned sound-quality model was adopted as an optimizing aim, when ASQC system worked and every gain factors was cyclically changed, the sound-quality annoyance grades of residual noise were continuously calculated and compared, and the best gain vector was determined as the lowest annoyance grade being obtained. Thereout, a simulation model of adaptive active sound-quality control system was developed and built with Matlab/Simulink, two real noise samples were active controlled and their sound quality improved obviously. This result proves that both the ASQC algorithm and the relevant control strategy are of good effect.
Based on the previous research, an adaptive active controller with the digital signal processor (DSP) as hardware key was developed, simultaneously the matching software of the system programmed with the standard C and assembly language was developed too. Thus the active sound-quality control of the interior noise can be tested in an actual automobile since both the hardware and software of the ASQC system with the binaural channels have been finished.
Finally, active sound-quality control of the vehicle interior noise was carried out in a local space near the two ears of assistant driver by means of the system developed. The experimental results indicate that the ASQC system is of the good stability and satisfactory convergence when the car running at four different velocities in the flat asphalt road. And both the loudness and sharpness, especially the former, of the interior noise evidently are better than before control, thereinto the loudness is reduced by 22%～32% and sharpness by 8%～13%. Consequently the sound quality is improved remarkably and the annoyance grade of the interior noise is dropped by more than 25%, which is verified by both objective compare according to the sound-quality model and subjective evaluation of the same jury.
开发了可计算响度、尖锐度、粗糙度和抖动度四个心理声学参量的声品质客观评价软件，并验证了所编程序的计算可靠度和准确度。
以等级评分方法对采自8种不同类型国产轿车的32个稳态车内声音样本进行了主观评价试验，再以回归分析方法，建立了以心理声学客观参量响度和尖锐度描述的声品质烦躁度数学模型。
提出了可实现车内声品质改善的控制方法和相应控制策略：通过引入增益系数对不同频段噪声实现程度不同的可控性抵消；以二级自适应滤波器降低随机成分干扰；并按照声品质指标最优化原则，确定适宜的增益系数向量。仿真建模分析证明，该方法及相应控制策略是有效的。
以DSP功能板为硬件核心，研制了自适应主动控制器，并开发了相应软件，在某国产轿车上实际构建了车内声品质自适应主动控制系统。
利用该系统对车内副驾驶员两耳旁噪声实施了主动控制试验，在汽车匀速行驶的四种不同稳态工况下，所建系统都能够有效降低车内噪声的响度和尖锐度，特别是响度的下降更为显著，相应的车内声品质烦躁度等级也下降了25％以上。
Consumers are often deeply affected by their subjective impression of vehicle interior noise when choosing a car, while A weight sound pressure level, as one evaluating indicator in the traditional research on automotive noise, can't fully show the annoyance grade of a driver or passenger to the vehicle interior noise. So the concept Sound Quality to reflect personal subjective feeling of noise arises, and has been one of the important indexes used to evaluate all kinds of sound. And the research on evaluation, analysis and control of sound quality has come to more and more attention.
The key to improve the sound quality of vehicle interior noise lies in reducing the specific noise components to put listeners to annoyance. In this aspect, active noise control (ANC) technique, based on the principle of superposition whereby an unwanted primary noise can be canceled by the additional secondary noise of equal amplitude and opposite phase, has a good application prospect because of the obvious effect on low-frequency noise, the strong especial purpose and the selective noise elimination.
The evaluation and control for the sound quality of vehicle interior noise was studied, and improving sound quality of the interior noise in a car with ANC method was attempted in this paper. First, an equation between the subjective evaluation and the objective psycho-acoustical parameters was gained. Then an active sound-quality control (ASQC) adaptive algorithm and the relevant control strategy were brought forward. After the ASQC system of binaural channels had been established, the active control experiments were conducted in a car at last. The main works completed in the paper are as follows:
The sound quality of automobile product can be described with psycho-acoustical objective parameters as lots of foreign research indicates, so the principle of choosing proper psycho-acoustical parameters suitable to interior noise characteristic was given, and by this means four parameters loudness, sharpness, roughness and fluctuation were selected for the subsequent analysis. Then a set of software to assess sound quality objectively with the aforementioned four parameters was developed by our group according to ISO532B and other common models. A group of steady noise signals from an automotive cabin were calculated with this software and ArtemiS, one of sound-quality software developed by German company Head Acoustics, and the contrast between the two results of four parameters shows a small difference. It is obvious that our software can be used to analysis and appraise the sound quality of automobile because of enough credibility and accuracy.
The 32 steady samples of vehicle interior noise near the ears of assistant driver from 8 types of cars at different gears and velocities in a flat asphalt road were selected as evaluating objects, and the subjective evaluation tests were carried out with the magnitude estimation method by a jury composed of 30 appraisers. After the evaluating data strictly verified, there are 6 persons' results were not accepted due to the significant discrepancy with others, and the left 24 ones' evaluating scores were averaged as annoyance grade of every sample. Then by means of correlation and regression analysis between the subjective appraisal results and four psycho-acoustical objective parameters of 32 sound samples, a sound-quality annoyance model with which the subjective evaluation be quantitatively expressed with objective parameters was established. The model shows that sound quality of the interior noise mainly depends on loudness and sharpness, especially the former, under the stable conditions that vehicles running at various uniform speeds. And it gives a direction for how to improve sound quality.
On the basis of the above-mentioned work, theoretical research and simulation analysis on an ANC system, its algorithm and the relevant control strategy for improving sound quality were done. At first, according to the close correlation between dominant noise components and vibrating acceleration signals of engine-body and auto-body suspension, the reference signals of interior noise were distinguished and predicted by Elman dynamic neural network (NN) that the vibrating acceleration signals were used as its input, and after operational factors of the Elman NN had been chose through the simulation compare, the acoustic feedback disturbance of feed-forward ANC system was successfully avoided.
The next work was finishing an active sound-quality control algorithm of the vehicle interior noise with the active noise equalization (ANE) and filtered-error least mean square (FELMS) technique as its core. The ASQC algorithm can make up the insufficiency of the traditional ANC system that the noise cancelled degree can not be controlled depending upon different frequency bands and be easily disturbed by random sound components, as a result of two prominent characteristics: First, gain factor, one new parameter, is applied to control the acoustic attenuation, and the noise of different frequency will be active cancelled to different degrees, if they have respective different gain factors. Second, passband disturbance caused by some random noise components uncorrelated to reference signals can be reduced through two-layer adaptive filters. Simulations under various parameters show remarkable performance of this ASQC algorithm.
After convergent rate of the ASQC algorithm and computing speed of psycho-acoustical parameters had been considered together, the active sound-quality control strategy of the vehicle interior noise was put forward: The primary noise was divided into some simple harmonic waves with different frequencies and amplitudes after being digitally filtered and frequency-divided, and these simple harmonic signals were active controlled at the respective gain factors corresponding to different critical bands. At the same time, the aforementioned sound-quality model was adopted as an optimizing aim, when ASQC system worked and every gain factors was cyclically changed, the sound-quality annoyance grades of residual noise were continuously calculated and compared, and the best gain vector was determined as the lowest annoyance grade being obtained. Thereout, a simulation model of adaptive active sound-quality control system was developed and built with Matlab/Simulink, two real noise samples were active controlled and their sound quality improved obviously. This result proves that both the ASQC algorithm and the relevant control strategy are of good effect.
Based on the previous research, an adaptive active controller with the digital signal processor (DSP) as hardware key was developed, simultaneously the matching software of the system programmed with the standard C and assembly language was developed too. Thus the active sound-quality control of the interior noise can be tested in an actual automobile since both the hardware and software of the ASQC system with the binaural channels have been finished.
Finally, active sound-quality control of the vehicle interior noise was carried out in a local space near the two ears of assistant driver by means of the system developed. The experimental results indicate that the ASQC system is of the good stability and satisfactory convergence when the car running at four different velocities in the flat asphalt road. And both the loudness and sharpness, especially the former, of the interior noise evidently are better than before control, thereinto the loudness is reduced by 22%～32% and sharpness by 8%～13%. Consequently the sound quality is improved remarkably and the annoyance grade of the interior noise is dropped by more than 25%, which is verified by both objective compare according to the sound-quality model and subjective evaluation of the same jury.
引文
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74 王登峰，刘宗巍，梁杰，王世刚，姜吉光．车内噪声品质的主观评价试验与客观量化描述研究．吉林大学学报 (工学版)，2006，增刊2：41～45
75 Martin Pflueger, Robert Hoeldrich, Franz K. Brandl and Werner Biermayer. Subjective Assessment of Roughness as a Basis for Objective Vehicle Interior Noise Quality Evaluation. SAE Paper, 1999-01-1850
76 马大猷．现代声学理论基础．科学出版社，2004，50～51
77 E. Zwicker, H. Fastl. Psychoacoustics: Facts and Models. Spring-Verlag Berlin Heidelberg, 1999
78 Brian C. J. Moore. An Introduction to the Psychology of Hearing. Academic Press, San Diego, 1997, Third Ed.
79 Fletcher H. Auditory Patterns. Rev. Mod. Phys, 1940, 12, 47～65
80 马大猷．声学手册．科学出版社，2004，修订第二版，560～577
81 Acoustics: Method for calculating loudness level. IS0532-1975
82 Procedure for calculating loudness level and loudness. DIN45631-1991
83 H. David. The Method of Paired Comparisons. Oxford University Press 1988
84 N. Kousgaard. The Application of Binary Paired Comparisons to Listening Tests. Symp. On perception of Reproduced Sound, Denmark 1987
85 S. Bech. Planning of a Listening Test - Choice of Rating Scale and Test Procedure. Symp. On perception of Reproduced Sound, Denmark 1987
86 ANSI S12.2-1995. Criteria for Evaluation Room Noise. American National Standard
87 Kendall M G, Smith B B. On the Method of Paired Comparisons. Biometrika, 1940, 31 (3/4), 324～345
88 Parizet E. Paired Comparison Listening Tests and Circular Error Rates. ACTA 2002, 88(4), 594～598
89 P. Sjostn. Experiences of an Adaptive Control System for Active Noise Control. Proceeding of Inter-noise 85, PP. 595-598
90 Nelson, P. A. Active Control of Sound. Academic Press, second printing, 1994.
91 何渝生主编．汽车噪声控制．机械工业出版社，1999，08
92 李树珉等编著．汽车噪声原理、检测与控制．中国环境科学出版社，1992，10
93 常振臣等．神经网络方法在车内噪声信号预测中的应用．农业机械学报，2003，1
94 Judd. J. S. Neural Network Design and the Complexity of Learning. The MIT, 1990
95 I. M. Mujtaba, M. A. Hussain. Application of Neural Networks and Other Learning Technologies in Process Engineering. Imperial College Press, 2001
96 陈克安，马远良．噪声源有源噪声控制—原理、算法及实现．西北工业大学出版社，1993
97 黎旭等．基于 LMS 自适应滤波的噪声抵消．云南民族学院学报(自然科学版)，2000，06
98 S.M. Kuo, M. J. Ji. Development and Analysis of an Adaptive Noise Equalizer. IEEE Transactions on Speech and Audio Processing, 1995,03,217～222
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100 S. M. Kuo, M. Tahernezhadi, L. Ji. Frequency-domain Periodic Active Noise Control and Equalization. IEEE Transactions on Speech and Audio Processing, 1997,05,348～358
101 S. M. Kuo, M. J. Ji. Passband Disturbance Reduction in Adaptive Narrowband Noise Control Systems. IEEE Transactions on Circuits System I, 1999,46,220～223
102 S. Mallu. Integration and Optimization of Active Noise Equalizer with Filtered Error Least Mean Square Algorithm. MS Thesis, Northern Illinois University, 2005
103 张翔等．自适应有源噪声控制算法的研究与实现．北京理工大学学报，2002，Vol．22，No．1
104 郭虎等．MATLAB 语言在控制系统仿真中的应用．长沙电力学院学报 (自然科学版)，1998，Vol．13，No．2
105 曾向阳．三维空间声场的计算机仿真．计算机仿真，2002，01
106 向小强．传声器 (一)．电声技术，2002 (4)
107 向小强．传声器 (二)．电声技术，2002 (6)
108 翁泰来．最新修订的扬声器国家标准．电声技术，1998 (6)
109 戴维．克拉克．扬声器参数的精确测量(一)．电声技术，1997 (9)
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72 Wang Dengfeng, Liu Zongwei, Chang Zhenchen and Liu Xueguang. Study on Adaptive Active Control of Unstable Noise in Vehicle, 2006, CMESM2006 Paper: 413～418
73 刘宗巍，王登峰，梁杰．车内噪声声质量的主观评价及其改善措施的研究与发展．汽车技术，2006，7
74 王登峰，刘宗巍，梁杰，王世刚，姜吉光．车内噪声品质的主观评价试验与客观量化描述研究．吉林大学学报 (工学版)，2006，增刊2：41～45
75 Martin Pflueger, Robert Hoeldrich, Franz K. Brandl and Werner Biermayer. Subjective Assessment of Roughness as a Basis for Objective Vehicle Interior Noise Quality Evaluation. SAE Paper, 1999-01-1850
76 马大猷．现代声学理论基础．科学出版社，2004，50～51
77 E. Zwicker, H. Fastl. Psychoacoustics: Facts and Models. Spring-Verlag Berlin Heidelberg, 1999
78 Brian C. J. Moore. An Introduction to the Psychology of Hearing. Academic Press, San Diego, 1997, Third Ed.
79 Fletcher H. Auditory Patterns. Rev. Mod. Phys, 1940, 12, 47～65
80 马大猷．声学手册．科学出版社，2004，修订第二版，560～577
81 Acoustics: Method for calculating loudness level. IS0532-1975
82 Procedure for calculating loudness level and loudness. DIN45631-1991
83 H. David. The Method of Paired Comparisons. Oxford University Press 1988
84 N. Kousgaard. The Application of Binary Paired Comparisons to Listening Tests. Symp. On perception of Reproduced Sound, Denmark 1987
85 S. Bech. Planning of a Listening Test - Choice of Rating Scale and Test Procedure. Symp. On perception of Reproduced Sound, Denmark 1987
86 ANSI S12.2-1995. Criteria for Evaluation Room Noise. American National Standard
87 Kendall M G, Smith B B. On the Method of Paired Comparisons. Biometrika, 1940, 31 (3/4), 324～345
88 Parizet E. Paired Comparison Listening Tests and Circular Error Rates. ACTA 2002, 88(4), 594～598
89 P. Sjostn. Experiences of an Adaptive Control System for Active Noise Control. Proceeding of Inter-noise 85, PP. 595-598
90 Nelson, P. A. Active Control of Sound. Academic Press, second printing, 1994.
91 何渝生主编．汽车噪声控制．机械工业出版社，1999，08
92 李树珉等编著．汽车噪声原理、检测与控制．中国环境科学出版社，1992，10
93 常振臣等．神经网络方法在车内噪声信号预测中的应用．农业机械学报，2003，1
94 Judd. J. S. Neural Network Design and the Complexity of Learning. The MIT, 1990
95 I. M. Mujtaba, M. A. Hussain. Application of Neural Networks and Other Learning Technologies in Process Engineering. Imperial College Press, 2001
96 陈克安，马远良．噪声源有源噪声控制—原理、算法及实现．西北工业大学出版社，1993
97 黎旭等．基于 LMS 自适应滤波的噪声抵消．云南民族学院学报(自然科学版)，2000，06
98 S.M. Kuo, M. J. Ji. Development and Analysis of an Adaptive Noise Equalizer. IEEE Transactions on Speech and Audio Processing, 1995,03,217～222
99 Feng JW, Gan WS. Adaptive active noise equalizer. IEEE Electronics Letters, 1997,33(18),1518～1519
100 S. M. Kuo, M. Tahernezhadi, L. Ji. Frequency-domain Periodic Active Noise Control and Equalization. IEEE Transactions on Speech and Audio Processing, 1997,05,348～358
101 S. M. Kuo, M. J. Ji. Passband Disturbance Reduction in Adaptive Narrowband Noise Control Systems. IEEE Transactions on Circuits System I, 1999,46,220～223
102 S. Mallu. Integration and Optimization of Active Noise Equalizer with Filtered Error Least Mean Square Algorithm. MS Thesis, Northern Illinois University, 2005
103 张翔等．自适应有源噪声控制算法的研究与实现．北京理工大学学报，2002，Vol．22，No．1
104 郭虎等．MATLAB 语言在控制系统仿真中的应用．长沙电力学院学报 (自然科学版)，1998，Vol．13，No．2
105 曾向阳．三维空间声场的计算机仿真．计算机仿真，2002，01
106 向小强．传声器 (一)．电声技术，2002 (4)
107 向小强．传声器 (二)．电声技术，2002 (6)
108 翁泰来．最新修订的扬声器国家标准．电声技术，1998 (6)
109 戴维．克拉克．扬声器参数的精确测量(一)．电声技术，1997 (9)
110 戴维．克拉克．扬声器参数的精确测量(二)．电声技术，1997 (10)
111 C．H．汉森，S．D．斯奈德著．仪垂杰等译．噪声和振动的主动控制．科学出版社，2002．03
112 Middleton. R. H, Goodwin. G. C. Digital Control and Estimation: A Unified Approach. Prentice Hall, Englewoode Cliffs, NJ
113 韩峰，李义军．Nyquist 采样频率下的频谱失真与采样定理．内蒙古工业大学学报，2002，Vol．21，No．4