工程机械司机室内噪声信号盲源分离及特性研究
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摘要
目前我国工业化建设进展迅速,工程机械设备在基础设施建设及工农业生产中发挥着突出作用。工程机械行业已发展成为我国机械工业中的第四大行业,国产工程机械设备技术水平获得了很大提升。随着工程机械行业的快速发展以及各国环保政策的大力推行,人们对于工程机械的综合性能提出了更高的要求,对于工程机械的舒适性和噪声控制的要求也越来越严格。工程机械司机室内噪声作为NVH(Noise、Vibration、Harshness)重要指标之一,越来越受到广泛的关注。为增强我国的工程机械产品国际竞争力,推动我国向着工程机械制造强国转变,对司机室内噪声问题进行研究具有重大的现实意义。
推土机司机室受到多种振动和噪声激励影响,各噪声源具有各自的时频特征。当在多个噪声源共存的情况下,降低司机室噪声,需要抑制最主要噪声源,才能取得明显的降噪效果。因此,确定噪声源是降低噪声限值的首要工作。由于多个噪声源的同时作用,测试采集得到的噪声信号包含多种复杂的瞬变非稳态信号和少数稳态信号的叠加,研究司机室振动噪声源及特性,是实现噪声源定位的重要基础。
研究发现司机室内噪声、振动信号是含有一定噪声的非平稳信号,采用小波等时频分析方法能够其进行详细的研究,但需依据信号特征反复调整参数才能取得较好的分析效果,而EEMD能够将非线性、非平稳信号自适应分解成为一族固有模态函数IMF,这将有效的提高振动、噪声信号分析效率。但EEMD方法也存在着参与运算的白噪声信号影响分解效果的问题。改进的EEMD方法即MEEMD方法,在保持EEMD分解的优点的同时,减小了参与辅助计算的白噪声残留,对IMF分量的模态分裂也有较好的抑制作用。本研究将MEEMD方法应用到推土机司机室内振动噪声信号特征分析上,可改进现司机室内振动噪声源特征分析效果。
推土机司机室内振动噪声源众多,且传入路径复杂,由于工程实际测试时相关测量使用的传感器数量限制,如何利用有限的观测信号识别多个源信号,是欠定盲识别的难题。研究表明,单通道信号经MEEMD分解,拓展为多个带有源信号的特征信息的IMF分量,从而实现欠定问题到正定问题的转换。本文将采集的振动、噪声信号的MEEMD分解结果作为冗余的观测分量,分别利用协方差矩阵特征值分析方法和信号稀疏性特征分析方法,探讨司机室内振动噪声源的数目估计,为实现司机室内振动噪声源准确识别提供研究基础。
利用IMF分量进行振动噪声源识别时,由于MEEMD方法分解得到的IMF分量之间总存在一定的耦合信息,需要对IMF信号进行解耦,即去除各IMF信号间的相关性。IMF分量组合的观测阵代表了多个源信号的线性混合,采用独立成分分析(ICA)从混合矩阵分离出若干相互独立的成分,但现有方法受数值计算迭代初值的影响具有不确定性,且基于负熵的固定点算法(Fixed-Point Algorithm-FICA)的稳定性需要改进,本研究将MEEMD和ICA方法相整合改进,将其应用于司机室内噪声源的分离和识别,并通过相干分析与时频分析相结合的技术对噪声源进行定位,最后从隔声和减振两方面入手对司机室内噪声的进行了综合治理。
综合以上问题的分析,本研究展开了以下内容的研究：
结合推土机的结构和司机室的特点,对司机室内噪声来源、噪声传播途径和产生机理进行了分析,对多个型号推土机司机室内振动、噪声信号进行初步分析,确定司机室内振动噪声信号大致特征。采用四种不同的小波函数对多个型号推土机司机室内振动、噪声信号进行了时频分析,确定最优的复Morlet小波参数,并与STFT时频分析比较分析效果,研究发现复Morlet小波和STFT变换各有优点,可根据信号的特点选择合适的时频分析工具。
将MEEMD方法引入推土机司机室振动噪声源特征识别研究中,结合相关系数分析筛选有效IMF分量组合。结合IMF分量时频特征分析,研究了型号I司机室内底板振动信号、耳旁噪声信号,分析与司机室内振动噪声信号密切相关的各IMF分量。采用IMF分量的能量变化指标研究了运行工况变化对IMF分量能量特征的影响情况,根据能量变化的情况确定转速工况对耳旁噪声各IMF分量的影响,对敏感的IMF分量进行追踪和定位,为进一步实现驾驶室内噪声治理提供分析基础。
利用MEEMD将单个观测信号拓展为带有多个源信号特征信息的IMF分量组合,将欠定盲分析问题转化为正定问题。采用两种不同的分析方法分别利用单通道观测信号进行了源数估计：(1)根据观测阵信号子空间维数估计原理,将特征值方法应用于IMF分量构成的观测信号的分析中,对源信号的个数进行估计；(2)根据观测信号在时频域的稀疏性原理,将IMF分量构成的观测信号进行时频域转换,提取信号的稀疏特征向量进行源数估计。采用两种分析方法对型号Ⅱ推土机司机室内耳旁噪声和底板振动信号进行了源数估计,并对得到噪声合成信号Simf和振动合成信号Vimf,进行时频分析,分析影响司机室内耳旁噪声的主要频率。
对MEEMD和ICA方法进行整合,通过添加迭代步长参数α对基于负熵的固定点算法进行了改进,结合分离性能指标,选择最优的参数设置。采用整合改进的MEEMD和ICA方法,结合相干分析与时频分析相结合的技术,对型号Ⅰ推土机司机室内噪声信号进行盲分离,并对型号Ⅰ推土机司机室内振动噪声进行了分步分阶段治理,有效降低了司机室内耳旁噪声。从隔声和减振两方面入手的分阶段的综合治理的实验结果也验证了对司机室内噪声的综合治理行之有效。通过改进减振系统有效降低了低速工况下的噪声水平,而隔声措施对于降低中高速工况下的噪声效果较明显。
本研究以某系列液压式履带推土机研究对象,实现了工程机械司机室内噪声盲源分离,以司机室内振动噪声源估计、噪声源识别和特性分析为主要内容,以现代信号处理方法MEEMD、ICA时频分析以及信号稀疏特征分析等多种方法为手段,对司机室内复杂声场内盲源分离方法及关键技术进行了研究。研究结论和提出的研究方法对其他类型的工程机械具有一定的借鉴意义。本研究还存在一定的不足,主要有以下两个问题需要继续研究和完善：
(1)研究了某系列3种型号推土机的司机室内振动噪声情况,但未涉及到其他类型的工程机械,研究范围还需要拓展,使文中提出的研究方法应用更为广泛；
(2) MEEMD分解对非平稳信号分析具有一定的优势,但存在计算效率低的问题,参数选择的设置也有待进一步优化,未来可以建立参数选择的优化指标,提高算法的计算效率。
Construction machinery industry has become the fourth largest mechanical industry in our country, domestic engineering machinery equipment technology won the big promotion. With the rapid development of construction machinery industry, and the national environmental protection policies pushed forward, higher performance requirements about construction machinery are proposed, which the construction machinery should be comfort and low noise emission. The rapid development of China's urban construction, the tearing down and building of city, and the construction of basic infrastructures, all of these directly bring the great increase amount of construction machinery. The engine of machinery discharges high level noise during working, for example, the inlet noise and exhaust noise, noise radiated from the body, and the noise from gears. As an important indicator of NVH (Noise, Vibration and Harshness), cabin noise in construction machinery raised more and more common concerns of consumers and manufacturers. About the forecast, measuring evaluation and control methods of construction machinery cabin noise, there were a large number of literatures on theoretical and experimental researches. In order to enhance the the global competitiveness of Chinese construction machinery industrial products, promote our country towards construction machinery manufacturing powerful country, the research of cab interior noise is great significance.
Bulldozer cab has affected by a variety of vibration and noise excitation, and each noise source has the respective time-frequency characteristic. In the case of coexistence of multiple noise sources, the main noise source should be identified, thus obvious effect of the cab noise reduction would be achieved. Therefore, identification of the noise source should take precedence to reduce noise emission. At the same time, there are multiple noise sources. So noise signals obtained by the test contain complex collection of a steady state and transient unsteady signals. The cab vibration noise sources and the characteristics of them, are the important foundation to realize noise source localization.
Using wavelet frequency analysis method the transient unsteady noise signal can be studied in detail, but need to repeatedly adjust the parameters according to the signal characteristics in order to obtain a better analysis of results. Ensemble empirical mode decomposition (EEMD) is employed to decompose nonlinear and non-stationary signals into a series of adaptive linear, smooth intrinsic mode function (IMF) signals which meet the requirements of the blind source separation. But the residuals of added white noise signals also influence decomposition results. Improved EEMD method namely MEEMD method, maintains the advantage of the EEMD method. And at the same time the participation auxiliary calculation of white noise is reduced. The phenomenon of IMF component modal split is also suppressed. In this research MEEMD method was applied to feature analysis of the bulldozer cab indoor vibration noise signal, which can improve the analysis efficiency.
Vibration noise sources of bulldozer driver cab indoor are numerous, and passed indoor through different paths. Under the engineering practice tests condition, quantity of measurement sensors is restricted. By limited observed vibration or noise signals to identify multiple sources, is underdetermined blind identification problem. Some studies show that single channel signal via MEEMD decomposition, expand to multiple IMFs, so as to achieve the transformation of underdetermined problem into the positive definite problem. Decomposition results of the vibration and noise signals were utilized as redundant observation components in this research work. Covariance matrix eigenvalue analysis method and signal sparse characteristics analysis method were applied respectively to discuss the number estimation of vibration noise indoor, and achieve the accurate identification.of driver indoor vibration noise.
When MEEMD method solution was appled to identify the vibration noise sources, there are always some coupling between IMFs. And the IMFs signal should be decoupled, which remove the correlation between the signal. IMF component represents a combination of observation matrix linear mixed signals from multiple sources. The independent component analysis (ICA) can isolated from mixed matrix several independent components, but the existing methods is influenced by the numerical iterative initial uncertain value, so the fixed point algorithm based on negative entropy stability may be improved. In this research MEEMD and ICA method was integrated, which applied to the cab indoor noise source separation and identification. And through the correlation analysis and the time-frequency analysis technology, noise source was located. Finally from the two aspects, sound insulation and vibration reduction have carried on the indoor noise comprehensive abatement.
Based on the analysis of above problems, this research carried out the following research works:
According to the characteristics of the structure of bulldozer, the mechanism of cab interior noise source and noise transmission was analyzed. The vibration and noise signals of multiple type bulldozer were analyzed, and the roughly characteristics of these signals were obtained. Using four different wavelet functions, multiple type bulldozer cab vibration and noise signal was reserched in time-frequency analysis. The optimal parameters of complex Morlet wavelet were determined. And calculation results of complex Morlet wavelet analysis were compared with STFT time-frequency analysis. Result shows that STFT and complex Morlet wavelet transform have their own advantages, the proper time-frequency analysis tool was selected according to the characteristics of the signal.
By MEEMD method and correlation coefficient analysis, the effective IMFs were selected. Combined with the time-frequency feature analysis of the IMF component, the type I driver cab floor's vibration signal and ear noise signal was studied, and each component of the IMF closely related to the cab vibration and noise signals was analyzed Using the IMF component energy index, the IMF component energy characteristics at various operation condition were studied. According to the different working condition, the influence energy index changed. So the effect of the engine speed change on each IMF component of ear noise signal was applied to track and locate sensitive IMF component, to provide further cab interior noise analysis.
Some studies show that single channel signal via MEEMD decomposition, expand to multiple IMFs, so as to achieve the transformation of underdetermined problem into the positive definite problem. By two different analysis methods source number estimation have been conducted:(1) According to the dimension estimation principle of observation array signal subspace, eigenvalue method was applied to signal analysis. The number of source signals was estimated based on the IMF component of observation signal.(2) According to the sparse principle of observation signal in the time-frequency domain, the IMF component of observation signal was converted from time domain to frequency domain and signal sparse feature vector was extracted to estimate the source number. Using two methods the number of sources estimation was applied to type Ⅱ bulldozer driver ear noise and indoor floor vibration signal. And by calculating synthetic signal Vimf/and Simf, the time-frequency analysis was carry on the main frequency affecting the driver indoor ear noise.
By means of adding the iteration step size parameter at a fixed point, ICA algorithmis was improved. And combined with separation performance index, the optimal parameter was selected. With improved MEEMD and ICA method, combining with correlation analysis and the time-frequency analysis technology, blind source separation reaerch of driver indoor noise signal was carried on the model I bulldozer. With suspension system adjusted step by step, the noise in driver indoor ear effectively reduced. By comprehensive abatement in two aspects of sound insulation and vibration reduction in stages, the experiment results proved that the comprehensive abatement is effective to reduce the interior noise. The improvement of the suspension system effectively reduced the noise level in low speed working conditions, and the sound insulation measures obviously reduced the noise in high speed working conditions.
In this paper we take a series of hydraulic bulldozers as the research object. With modern signal processing methods as MEEMD, ICA, time-frequency analysis, signal sparse feature analysis and other methods, blind source separation method for complex acoustic field in the driver indoor are studied. And achieved blind source separation of the interior noise in their driver cab. Research conclusion and research methods are significant references for other types of construction machinery
Some deficiencies still exist in the research work, and two following problems need to research and improve:
(1) Three types of bulldozer driver indoor vibration noise was researched, but not involved in other types of construction machinery. Research scope need to expand so that the proposed method may more widely applied.(2) For non-stationary signal analysis, MEEMD method has certain advantages. But the problem of low efficiency in calculation and parameter selection also remains to be further optimized. In the future optimization index of parameter selection can be established to improve the computational efficiency of the algorithm.
推土机司机室受到多种振动和噪声激励影响,各噪声源具有各自的时频特征。当在多个噪声源共存的情况下,降低司机室噪声,需要抑制最主要噪声源,才能取得明显的降噪效果。因此,确定噪声源是降低噪声限值的首要工作。由于多个噪声源的同时作用,测试采集得到的噪声信号包含多种复杂的瞬变非稳态信号和少数稳态信号的叠加,研究司机室振动噪声源及特性,是实现噪声源定位的重要基础。
研究发现司机室内噪声、振动信号是含有一定噪声的非平稳信号,采用小波等时频分析方法能够其进行详细的研究,但需依据信号特征反复调整参数才能取得较好的分析效果,而EEMD能够将非线性、非平稳信号自适应分解成为一族固有模态函数IMF,这将有效的提高振动、噪声信号分析效率。但EEMD方法也存在着参与运算的白噪声信号影响分解效果的问题。改进的EEMD方法即MEEMD方法,在保持EEMD分解的优点的同时,减小了参与辅助计算的白噪声残留,对IMF分量的模态分裂也有较好的抑制作用。本研究将MEEMD方法应用到推土机司机室内振动噪声信号特征分析上,可改进现司机室内振动噪声源特征分析效果。
推土机司机室内振动噪声源众多,且传入路径复杂,由于工程实际测试时相关测量使用的传感器数量限制,如何利用有限的观测信号识别多个源信号,是欠定盲识别的难题。研究表明,单通道信号经MEEMD分解,拓展为多个带有源信号的特征信息的IMF分量,从而实现欠定问题到正定问题的转换。本文将采集的振动、噪声信号的MEEMD分解结果作为冗余的观测分量,分别利用协方差矩阵特征值分析方法和信号稀疏性特征分析方法,探讨司机室内振动噪声源的数目估计,为实现司机室内振动噪声源准确识别提供研究基础。
利用IMF分量进行振动噪声源识别时,由于MEEMD方法分解得到的IMF分量之间总存在一定的耦合信息,需要对IMF信号进行解耦,即去除各IMF信号间的相关性。IMF分量组合的观测阵代表了多个源信号的线性混合,采用独立成分分析(ICA)从混合矩阵分离出若干相互独立的成分,但现有方法受数值计算迭代初值的影响具有不确定性,且基于负熵的固定点算法(Fixed-Point Algorithm-FICA)的稳定性需要改进,本研究将MEEMD和ICA方法相整合改进,将其应用于司机室内噪声源的分离和识别,并通过相干分析与时频分析相结合的技术对噪声源进行定位,最后从隔声和减振两方面入手对司机室内噪声的进行了综合治理。
综合以上问题的分析,本研究展开了以下内容的研究：
结合推土机的结构和司机室的特点,对司机室内噪声来源、噪声传播途径和产生机理进行了分析,对多个型号推土机司机室内振动、噪声信号进行初步分析,确定司机室内振动噪声信号大致特征。采用四种不同的小波函数对多个型号推土机司机室内振动、噪声信号进行了时频分析,确定最优的复Morlet小波参数,并与STFT时频分析比较分析效果,研究发现复Morlet小波和STFT变换各有优点,可根据信号的特点选择合适的时频分析工具。
将MEEMD方法引入推土机司机室振动噪声源特征识别研究中,结合相关系数分析筛选有效IMF分量组合。结合IMF分量时频特征分析,研究了型号I司机室内底板振动信号、耳旁噪声信号,分析与司机室内振动噪声信号密切相关的各IMF分量。采用IMF分量的能量变化指标研究了运行工况变化对IMF分量能量特征的影响情况,根据能量变化的情况确定转速工况对耳旁噪声各IMF分量的影响,对敏感的IMF分量进行追踪和定位,为进一步实现驾驶室内噪声治理提供分析基础。
利用MEEMD将单个观测信号拓展为带有多个源信号特征信息的IMF分量组合,将欠定盲分析问题转化为正定问题。采用两种不同的分析方法分别利用单通道观测信号进行了源数估计：(1)根据观测阵信号子空间维数估计原理,将特征值方法应用于IMF分量构成的观测信号的分析中,对源信号的个数进行估计；(2)根据观测信号在时频域的稀疏性原理,将IMF分量构成的观测信号进行时频域转换,提取信号的稀疏特征向量进行源数估计。采用两种分析方法对型号Ⅱ推土机司机室内耳旁噪声和底板振动信号进行了源数估计,并对得到噪声合成信号Simf和振动合成信号Vimf,进行时频分析,分析影响司机室内耳旁噪声的主要频率。
对MEEMD和ICA方法进行整合,通过添加迭代步长参数α对基于负熵的固定点算法进行了改进,结合分离性能指标,选择最优的参数设置。采用整合改进的MEEMD和ICA方法,结合相干分析与时频分析相结合的技术,对型号Ⅰ推土机司机室内噪声信号进行盲分离,并对型号Ⅰ推土机司机室内振动噪声进行了分步分阶段治理,有效降低了司机室内耳旁噪声。从隔声和减振两方面入手的分阶段的综合治理的实验结果也验证了对司机室内噪声的综合治理行之有效。通过改进减振系统有效降低了低速工况下的噪声水平,而隔声措施对于降低中高速工况下的噪声效果较明显。
本研究以某系列液压式履带推土机研究对象,实现了工程机械司机室内噪声盲源分离,以司机室内振动噪声源估计、噪声源识别和特性分析为主要内容,以现代信号处理方法MEEMD、ICA时频分析以及信号稀疏特征分析等多种方法为手段,对司机室内复杂声场内盲源分离方法及关键技术进行了研究。研究结论和提出的研究方法对其他类型的工程机械具有一定的借鉴意义。本研究还存在一定的不足,主要有以下两个问题需要继续研究和完善：
(1)研究了某系列3种型号推土机的司机室内振动噪声情况,但未涉及到其他类型的工程机械,研究范围还需要拓展,使文中提出的研究方法应用更为广泛；
(2) MEEMD分解对非平稳信号分析具有一定的优势,但存在计算效率低的问题,参数选择的设置也有待进一步优化,未来可以建立参数选择的优化指标,提高算法的计算效率。
Construction machinery industry has become the fourth largest mechanical industry in our country, domestic engineering machinery equipment technology won the big promotion. With the rapid development of construction machinery industry, and the national environmental protection policies pushed forward, higher performance requirements about construction machinery are proposed, which the construction machinery should be comfort and low noise emission. The rapid development of China's urban construction, the tearing down and building of city, and the construction of basic infrastructures, all of these directly bring the great increase amount of construction machinery. The engine of machinery discharges high level noise during working, for example, the inlet noise and exhaust noise, noise radiated from the body, and the noise from gears. As an important indicator of NVH (Noise, Vibration and Harshness), cabin noise in construction machinery raised more and more common concerns of consumers and manufacturers. About the forecast, measuring evaluation and control methods of construction machinery cabin noise, there were a large number of literatures on theoretical and experimental researches. In order to enhance the the global competitiveness of Chinese construction machinery industrial products, promote our country towards construction machinery manufacturing powerful country, the research of cab interior noise is great significance.
Bulldozer cab has affected by a variety of vibration and noise excitation, and each noise source has the respective time-frequency characteristic. In the case of coexistence of multiple noise sources, the main noise source should be identified, thus obvious effect of the cab noise reduction would be achieved. Therefore, identification of the noise source should take precedence to reduce noise emission. At the same time, there are multiple noise sources. So noise signals obtained by the test contain complex collection of a steady state and transient unsteady signals. The cab vibration noise sources and the characteristics of them, are the important foundation to realize noise source localization.
Using wavelet frequency analysis method the transient unsteady noise signal can be studied in detail, but need to repeatedly adjust the parameters according to the signal characteristics in order to obtain a better analysis of results. Ensemble empirical mode decomposition (EEMD) is employed to decompose nonlinear and non-stationary signals into a series of adaptive linear, smooth intrinsic mode function (IMF) signals which meet the requirements of the blind source separation. But the residuals of added white noise signals also influence decomposition results. Improved EEMD method namely MEEMD method, maintains the advantage of the EEMD method. And at the same time the participation auxiliary calculation of white noise is reduced. The phenomenon of IMF component modal split is also suppressed. In this research MEEMD method was applied to feature analysis of the bulldozer cab indoor vibration noise signal, which can improve the analysis efficiency.
Vibration noise sources of bulldozer driver cab indoor are numerous, and passed indoor through different paths. Under the engineering practice tests condition, quantity of measurement sensors is restricted. By limited observed vibration or noise signals to identify multiple sources, is underdetermined blind identification problem. Some studies show that single channel signal via MEEMD decomposition, expand to multiple IMFs, so as to achieve the transformation of underdetermined problem into the positive definite problem. Decomposition results of the vibration and noise signals were utilized as redundant observation components in this research work. Covariance matrix eigenvalue analysis method and signal sparse characteristics analysis method were applied respectively to discuss the number estimation of vibration noise indoor, and achieve the accurate identification.of driver indoor vibration noise.
When MEEMD method solution was appled to identify the vibration noise sources, there are always some coupling between IMFs. And the IMFs signal should be decoupled, which remove the correlation between the signal. IMF component represents a combination of observation matrix linear mixed signals from multiple sources. The independent component analysis (ICA) can isolated from mixed matrix several independent components, but the existing methods is influenced by the numerical iterative initial uncertain value, so the fixed point algorithm based on negative entropy stability may be improved. In this research MEEMD and ICA method was integrated, which applied to the cab indoor noise source separation and identification. And through the correlation analysis and the time-frequency analysis technology, noise source was located. Finally from the two aspects, sound insulation and vibration reduction have carried on the indoor noise comprehensive abatement.
Based on the analysis of above problems, this research carried out the following research works:
According to the characteristics of the structure of bulldozer, the mechanism of cab interior noise source and noise transmission was analyzed. The vibration and noise signals of multiple type bulldozer were analyzed, and the roughly characteristics of these signals were obtained. Using four different wavelet functions, multiple type bulldozer cab vibration and noise signal was reserched in time-frequency analysis. The optimal parameters of complex Morlet wavelet were determined. And calculation results of complex Morlet wavelet analysis were compared with STFT time-frequency analysis. Result shows that STFT and complex Morlet wavelet transform have their own advantages, the proper time-frequency analysis tool was selected according to the characteristics of the signal.
By MEEMD method and correlation coefficient analysis, the effective IMFs were selected. Combined with the time-frequency feature analysis of the IMF component, the type I driver cab floor's vibration signal and ear noise signal was studied, and each component of the IMF closely related to the cab vibration and noise signals was analyzed Using the IMF component energy index, the IMF component energy characteristics at various operation condition were studied. According to the different working condition, the influence energy index changed. So the effect of the engine speed change on each IMF component of ear noise signal was applied to track and locate sensitive IMF component, to provide further cab interior noise analysis.
Some studies show that single channel signal via MEEMD decomposition, expand to multiple IMFs, so as to achieve the transformation of underdetermined problem into the positive definite problem. By two different analysis methods source number estimation have been conducted:(1) According to the dimension estimation principle of observation array signal subspace, eigenvalue method was applied to signal analysis. The number of source signals was estimated based on the IMF component of observation signal.(2) According to the sparse principle of observation signal in the time-frequency domain, the IMF component of observation signal was converted from time domain to frequency domain and signal sparse feature vector was extracted to estimate the source number. Using two methods the number of sources estimation was applied to type Ⅱ bulldozer driver ear noise and indoor floor vibration signal. And by calculating synthetic signal Vimf/and Simf, the time-frequency analysis was carry on the main frequency affecting the driver indoor ear noise.
By means of adding the iteration step size parameter at a fixed point, ICA algorithmis was improved. And combined with separation performance index, the optimal parameter was selected. With improved MEEMD and ICA method, combining with correlation analysis and the time-frequency analysis technology, blind source separation reaerch of driver indoor noise signal was carried on the model I bulldozer. With suspension system adjusted step by step, the noise in driver indoor ear effectively reduced. By comprehensive abatement in two aspects of sound insulation and vibration reduction in stages, the experiment results proved that the comprehensive abatement is effective to reduce the interior noise. The improvement of the suspension system effectively reduced the noise level in low speed working conditions, and the sound insulation measures obviously reduced the noise in high speed working conditions.
In this paper we take a series of hydraulic bulldozers as the research object. With modern signal processing methods as MEEMD, ICA, time-frequency analysis, signal sparse feature analysis and other methods, blind source separation method for complex acoustic field in the driver indoor are studied. And achieved blind source separation of the interior noise in their driver cab. Research conclusion and research methods are significant references for other types of construction machinery
Some deficiencies still exist in the research work, and two following problems need to research and improve:
(1) Three types of bulldozer driver indoor vibration noise was researched, but not involved in other types of construction machinery. Research scope need to expand so that the proposed method may more widely applied.(2) For non-stationary signal analysis, MEEMD method has certain advantages. But the problem of low efficiency in calculation and parameter selection also remains to be further optimized. In the future optimization index of parameter selection can be established to improve the computational efficiency of the algorithm.
引文
[1]胡浩.《工程机械噪声限值》修订标准解读与分析[J].工程机械,2011,(02)：64-65+68+64.
[2]SUZUKI Y, FUJII Y, WATARI A. REDUCTION OF INTERIOR CAR NOISE BY USING VECTOR METHOD [J].1978, v (3):1447-1462.
[3]KIM J, VARADAN V V, KO B. Finite element modeling of active cabin noise control problems; proceedings of the Smart Materials, Structures, and MEMS, December 11,1996-December 14,1996, Bangalore, India, F,1996 [C]. SPIE.
[4]MORRISON H B, CASALI J G. Intelligibility of synthesized voice messages in commercial truck cab noise for normal-hearing and hearing-impaired listeners [J]. International Journal of Speech Technology,1997,2 (1):33-44.
[5]KIM J, KO B, LEE J, et al. Optimal design of piezoelectric smart structures for active cabin noise control; proceedings of the Smart Structures and Materials 1998 Mathematics and Control in Smart Structures, May 2,1998-May 5,1998, San Diego, CA, United states, F,1998 [C]. SPIE.
[6]KIM J, KO B, LEE J-K, et al. Finite element modeling of a piezoelectric smart structure for the cabin noise problem [J]. Smart Materials and Structures,1999,8 (3):380-389.
[7]GOPINATHAN S V, VARADAN V V, VARADAN V K. Finite element/boundary element simulation of interior noise control using active-passive control technique [M]//VARADAN V V. Smart Structures and Materials 2000:Mathematics and Control in Smart Structures.2000:22-32.
[8]LEE D H, HWANG W S, KIM C M. Design sensitivity analysis and optimization of an engine mount system using an FRF-based substructuring method [J]. Journal of Sound and Vibration,2002,255 (2):383-397.
[9]SONG C K, HWANG J K, LEE J M, et al. Active vibration control for structural-acoustic coupling system of a 3-D vehicle cabin model [J]. Journal of Sound and Vibration,2003,267 (4):851-865.
[10]DIAZ J, EGANA J M, VINOLAS J. A local active noise control system based on a virtual-microphone technique for railway sleeping vehicle applications [J]. Mechanical Systems and Signal Processing,2006,20 (8):2259-2276.
[11]THOMAS J K, LOVSTEDT S, BLOTTERS J D, et al. Eigenvalue equalization filtered-x (EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin [J]. Noise Control Engineering Journal,2008,56 (1):25-34.
[12]LALOR N, PRLEBSCH H H. The prediction of low-and mid-frequency internal road vehicle noise:A literature survey [J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2007,221 (3): 245-269.
[13]KAWANO J, AMAKASU J, TANAKA T. Noise detection technology development for car cabin; proceedings of the 2008 World Congress, April 14,2008-April 17, 2008, Detroit, MI, United states, F,2008 [C]. SAE International.
[14]HOSSEINI FOULADIM, NOR M J M, ARIFFIN A K. Spectral analysis methods for vehicle interior vibro-acoustics identification [J]. Mechanical Systems and Signal Processing,2009,23 (2):489-500.
[15]KERKETTA S, BAGH S. Noise levels inside the cabin of heavy earth moving machineries:Its significance and association [J]. Noise and Vibration Worldwide, 2011,42(5):14-19.
[16]ALI M E H R, ATTARI M A. Design and implementation of ANC algorithm for engine noise reduction inside an automotive cabin using TMS320C5510; proceedings of the 2011 19th Iranian Conference on Electrical Engineering, ICEE 2011, May 17,2011-May 19,2011, Tehran, Iran, F,2011 [C]. IEEE Computer Society.
[17]YUKSEL E, KAMCI G, BASDOGAN I. Vibro-acoustic design optimization study to improve the sound pressure level inside the passenger Cabin [J]. Journal of Vibration and Acoustics, Transactions of the ASME,2012,134 (6).
[18]MUHAMAD W Z A W, JUNOH A K. Optimization of noise and vibration in passenger car cabin; proceedings of the 2012 IEEE Colloquium on Humanities, Science and Engineering Research, CHUSER 2012, December 3,2012 December 4,2012, Kota Kinabalu, Sabah, Malaysia, F,2012 [C]. IEEE Computer Society.
[19]AIRAKSINEN T, TOIVANEN J. An optimal local active noise control method based on stochastic finite element models [J]. Journal of Sound and Vibration, 2013,332 (26):6924-6933.
[20]KULAH S, ARIDOGAN U, BASDOGAN I. Investigation of an Active Structural Acoustic Control System on a Complex 3D Structure; proceedings of the 31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013, February 11,2013-February 14,2013, Garden Grove, CA, United states, F,2014 [C]. Springer New York.
[21]GOODWIN D W. Low frequency noise in motor cars-study of origins and design parameters [D]. Ph.D. Thesis:University of Southampton,1968.
[22]JHA S K. CHARACTERISTICS AND SOURCES OF NOISE AND VIBRATION AND THEIR CONTROL IN MOTOR CARS [J]. Journal of Sound and Vibration, 1976,48 (4):543-558.
[23]蔡应强,陈清林.国内外推土机的发展现状及趋势[J].机床与液压,2013,(08)：133-136.
[24]WU J-D, LEE T-H, BAI M R. Background noise cancellation for hands-free communication system of car cabin using adaptive feedforward algorithms [J]. International Journal of Vehicle Design,2003,31 (4):440-451.
[25]WILLINGALE B J. LOW FREQUENCY SOUND IN LOCOMOTIVE DRIVING CABINS; proceedings of the 10th International Congress on Acoustics Volume 3: Contributed Papers Continued, Sydney, Aust, F,1980 [C].
[26]WANG L, GAN W S, CHONG Y K, et al. A novel approach to bass enhancement in automobile cabin; proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, May 14,2006-May 19, 2006, Toulouse, France, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[27]WANG C Y, VAICAITIS R. Active control of vibrations and noise of double wall cylindrical shells [J]. Journal of Sound and Vibration,1998,216 865-888.
[28]VENEGAS R, NABUCO M, MASSARANI P. Sound insulation evaluation using transfer function measurements; proceedings of the 34th International Congress on Noise Control Engineering 2005, INTERNOISE 2005, August 7,2005- August 10,2005, Rio de Janeiro, Brazil, F,2005 [C]. Institute of Noise Control Engineering of the USA.
[29]UTSUNO H, TANAKA T. Reduction of acoustic standing waves in construction machine cabins by tuned vinyl covered urethane absorbing materials [J]. Proceedings-International Conference on Noise Control Engineering,1988, 1467-1467.
[30]NAKKULA JR W, ZIMMERLI R, BOROUGH R. An example of noise control treatment for construction Machinery Cab Interiors; proceedings of the Earthmoving Industry Conference, April 6,1981-April 8,1981, Peoria, IL, United states, F,1981 [C]. SAE International.
[31]TANAKA T, UTSUNO H, TARUYAMA K, et al. Techniques to control construction machinery noise [J]. R and D:Research and Development Kobe Steel Engineering Reports,1988,38 (3):6-9.
[32]DESMET W, PLUYMERS B, SAS P. Vibro-acoustic analysis procedures for the evaluation of the sound insulation characteristics of agricultural machinery cabins [J]. Journal of Sound and Vibration,2003,266 (3):407-441.
[33]GUJARATHI R N, KOLTE K S, KSHIRSAGAR K G, et al. Panel contribution study of a commercial excavator cab using sound intensity measurements; proceedings of the 22nd National Conference on Noise Control Engineering, NOISE-CON 2007, October 22,2007-October 24,2007, Reno, NV, United states, F,2007 [C]. Institute of Noise Control Engineering of the USA.
[34]SHIN C W, PARK S Y, KANG Y J, et al. Interior noise reduction of wheel loader using Boundary Element Method; proceedings of the 15th International Congress on Sound and Vibration 2008, ICSV 2008, July 6,2008-July 10,2008, Daejeon, Korea, Republic of, F,2008 [C]. International Institute of Acoustics and Vibrations.
[35]WILLEMSEN A M, PORADEK F, RAO M D. Reduction of noise in an excavator cabin using order tracking and ultrasonic leak detection [J]. Noise Control Engineering Journal,2009,57 (5):400-412.
[36]KIM K T, KIM H T, JOO W H. Low noise cabin design for construction equipment; proceedings of the SAE 2013 Noise and Vibration Conference and Exhibition, NVC 2013, May 20,2013-May 23,2013, Grand Rapids, MI, United states, F, 2013 [C]. SAE International.
[37]马燕,陈剑,袁正.矿用自卸汽车驾驶室噪声源识别[J].噪声与振动控制,2012,(03)：118-120+132.
[38]姜义林,李世伟,刘建美.隔振器在工程机械驾驶室噪声控制中的应用[J].山东理工大学学报(自然科学版),2005,(05)：73-76.
[39]张瑞.工程机械驾驶室新材料与新工艺的应用[J].工程机械,2012,(04)：58-63+59.
[40]张立军周余靳.发动机振动引起的车内噪声控制研究[J].振动、测试与诊断,2001,(01)：61-66+76.
[41]于坚.传动轴的振动及驾驶室噪声[J].汽车技术,1988,(02)：12-15.
[42]徐心虹.装载机驾驶室的噪声控制[J].工程机械,1991,(09)：24-27+51.
[43]陆森林,宫镇.拖拉机驾驶室内噪声分析[J].江苏工学院学报,1990,(03)：43-50.
[44]封磊,方玉莹,左言言.某叉车驾驶室外辐射声场预测研究[J].科学技术与工程,2013,(25)：7422-7426.
[45]常振臣王周郭.车内噪声控制技术研究现状及展望[J].吉林工业大学学报(工学版),2002,(04)：86-90.
[46]丁玉兰.工程机械的重要研究课题——机械噪声[J].工程机械,1981,(06)：52-54.
[47]蔡世彦.国内外工程机械司机室现状分析[J].工程机械,1988,(06)：41-45+11+54.
[48]朱桂华,宫镇,胡均平,等.统计能量分析用于工程机械驾驶室噪声预估[J].中南工业大学学报(自然科学版),2003,(02)：166-169.
[49]邓习树,邵威,黄志亮.工程机械驾驶室内部噪声预估分析[J].中国工程机械学报,2012,(04)：458-462.
[50]蒋丰鑫,陈剑,肖悦.挖掘机驾驶室声-固耦合系统低频噪声分析[J].工程机械,2013,(08)：25-30+42.
[51]易小刚,邓习树.工程机械驾驶室内部噪声源识别的小波分析方法[J].中国工程机械学报,2009,(03)：265-269.
[52]金岩,常志权.频谱分析技术在车辆nvh故障诊断中的应用[J].噪声与振动控制,2011,(01)：110-113.
[53]陈心昭.噪声源识别技术的进展[J].合肥工业大学学报(自然科学版),2009,(05)：609-614.
[54]MAYNARD J D, WILLIAMS E G, LEE Y. Nearfield acoustic holography. I. Theory of generalized holography and the development of NAH [J]. Journal of the Acoustical Society of America,1985,78 (4):1395-1413.
[55]WILLIAMS E G, DARDY H D, WASHBURN K B. Generalized nearfield acoustical holography for cylindrical geometry:theory and experiment [J]. Journal of the Acoustical Society of America,1987,81 (2):389-407.
[56]XIANG C-L, CHEN F-Z, LIU H, et al. Experimental research on noise source identification and the radiation characteristics of a gearbox based on NAH [J]. Binggong Xuebao/Acta Armamentarii,2009,30 (11):1424-1429.
[57]ZHOU L, DING S, ZHANG R, et al. Effect on measurement of the radiated acoustic field of underwater structure based on NAH with swinging measuring surface; proceedings of the International Conference on Mechanical Science and Engineering, ICMSE 2012, July 20,2012-July 22,2012, Beijing, China, F,2012 [C]. Trans Tech Publications.
[58]ZHANG D, ZHU N, CHENG J, et al. Experimental research on vibrating objects and its radiation field using near-field acoustic holography [J]. Shengxue Xuebao/Acta Acustica,1995,20 (4):250-255.
[59]WILLIAMS E G, HOUSTON B H, HERDIC P C. Reconstruction of the surface velocity and interior acoustic intensity from an aircraft fuselage using nearfield acoustical holography; proceedings of the Proceedings of the 1996 National Conference on Noise Control Engineering Part 2 (of 2), September 29,1996-October 2,1996, Bellevue, WA, USA, F,1996 [C]. Inst of Noise Control Engineering.
[60]WANG X-Q, YANG S-F, ZHAO J-M, et al. Application research on nah for identification of plant sound source; proceedings of the 2008 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2008, December 5,2008-December 8,2008, Nanjing, China, F,2008 [C]. Inst. of Elec. and Elec. Eng. Computer Society.
[61]WAN Q, JIANG W K. Near field acoustic holography (NAH) theory for cyclostationary sound field and its application [J]. Journal of Sound and Vibration, 2006,290 (3-5):956-967.
[62]TAKATA H, HALLMAN D, BOLTON J S. Research on nearfield acoustic holography (Selection strategies for optimal reference sets using singular value decomposition) [J]. Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C,1995,61 (584):1503-1508.
[63]SUN X-W, CHEN Q-F, CHEN X-R, et al. Ab initio study of phase transition and bulk modulus of NaH [J]. Journal of Solid State Chemistry,2011,184 (2): 427-431.
[64]IH J-G, KIM B-K. On the use of the BEM-based NAH for the vibro-acoustic source imaging on the nonregular exterior surfaces; proceedings of the Proceedings of the 1998 National Conference on Noise Control Engineering Part 1 (of 3), April 5,1998-April 9,1998, Ypsilanti, MI, USA, F,1998 [C]. Inst Noise Control Eng.
[65]HALD J. Combined NAH and beamforming using the same microphone array [J]. Sound and Vibration,2004,38 (12):18-27.
[66]DUMBACHER S M, BROWN D L, BLOUGH J R, et al. Practical aspects of making NAH measurements; proceedings of the Noise and Vibration Conference and Exposition, May 17,1999-May 20,1999, Traverse City, MI, United states, F, 1999 [C]. SAE International.
[67]BHATTACHARJEE A, DASTIDAR K R. Control of de-excitation to selected vibrational levels in the ground state of NaH molecule using two broadband ultrashort pulses [J]. Molecular Physics,2006,104 (15):2485-2495.
[68]LI Z, FENG T, HUANG Z, et al. The noise source localization of industrial sewing machine by NAH method; proceedings of the 2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012, December 8,2012-December 9,2012, Taiwan, F,2013 [C]. Trans Tech Publications.
[69]褚志刚,杨洋,倪计民,等.波束形成声源识别技术研究进展[J].声学技术,2013,(05)：430-435.
[70]褚志刚,蔡鹏飞,蒋忠翰,等.基于声阵列技术的柴油机噪声源识别[J].农业工程学报,2014,(02)：23-30.
[71]杨洋,倪计民,褚志刚,等.基于波束形成的发动机噪声源识别及声功率计算[J].内燃机工程,2013,(03)：39-43+49.
[72]周卫东,贾磊.小波变换和独立分量分析去除脑电信号中的噪声和干扰[J].山东大学学报(医学版),2003,(02)：116-119+122.
[73]钟飞,谭中军,史铁林,等.基于ica和小波变换的轴承故障特征提取[J].微计算机信息,2007,(28)：154-155+269.
[74]李秀坤,李婷婷,夏峙.水下目标特性特征提取及其融合[J].哈尔滨工程大学学报,2010,(07)：903-908.
[75]胡伊贤,李舜酩,张袁元,等.车辆噪声源识别方法综述[J].噪声与振动控制,2012,(05)：11-15+20.
[76]张俊红,李林洁,刘海,等.基于经验模态分解和独立成分分析的柴油机噪声源识别技术[J].内燃机学报,2012,(06)：544-549.
[77]张波,毕传兴,徐亮.基于人工神经网络模型的车门关闭声声品质评价方法研究[J].汽车工程,2011,(11)：1003-1006.
[78]王保平,王增才,张万枝.基于emd与神经网络的煤岩界面识别方法[J].振动测试与诊断,2012,(04)：586-590+688.
[79]杨健,李颖晖,熊大顺.基于小波包能量谱和改进神经网络的异步电机故障诊断[J].电力电子,2013,(02)：19-21+31.
[80]刘冰,张梅.车内噪声源识别技术的研究[J].机械管理开发,2013,(03)：13-15.
[81]刘建敏,李晓磊,乔新勇,等.基于emd和stft柴油机缸盖振动信号时频分析[J].噪声与振动控制,2013,(02)：133-137.
[82]赵志宏,杨绍普,申永军.基于独立分量分析与相关系数的机械故障特征提取[J].振动与冲击,2013,(06)：67-72.
[83]HAMMOND J K, WHITE P R. Analysis of non-stationary signals using time-frequency methods [J]. Journal of Sound and Vibration,1996,190 (3): 419-447.
[84]董建华,顾汉明,张星.几种时频分析方法的比较及应用[J].工程地球物理学报,2007,(04)：312-316.
[85]邹红星,周小波,李衍达.时频分析：回溯与前瞻[J].电子学报,2000,(09)：78-84.
[86]郭奇,刘卜瑜,史立波,等.基于二次EEMD的Wigner-Ville分布旋转机械故障信号分析及试验研究[J].振动与冲击,2012,(13)：129-133+153.
[87]宁静,诸昌钤,张兵.基于EMD和Cohen核的轨道不平顺信号分析方法[J].振动与冲击,2013,(04)：31-38.
[88]UCHIDA H, UEDA K. Detection of transient noise of car interior using non-stationary signal analysis; proceedings of the 1998 SAE International Congress and Exposition, February 23,1998-February 26,1998, Detroit, MI, United states, F,1998 [C]. SAE International.
[89]ISHIMITSU S, KOBAYASHI H. Study on instantaneous correlation analyses of acceleration car interior noise using wavelets and its subjective evaluation [J]. Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C,2006,72 (7):2094-2100.
[90]ISHIMITSU S, TAKAMI K, SAKAMOTO K, et al. Study on the contribution of intake noise using complex time-time analysis and subjective evaluation [J]. International Journal of Wavelets, Multiresolution and Information Processing, 2010,8 (4):609-625.
[91]RUBIO E, JAUREGUI J C. Experimental characterization of mechanical vibrations and acoustical noise generated by defective automotive wheel hub bearings [M]//ZUPPA L A, COVARRUBIAS R D H, ANDRES M V, et al. International Meeting of Electrical Engineering Research 2012.2012:176-181.
[92]余烽,徐中明,周小林,等.基于复Morlet小波的混合动力客车噪声识别与改进[J].机械传动,2013,(07)：88-90+93.
[93]黄志雄,何清华.液压挖掘机反铲切削过程振动信号去噪处理[J].中南大学学报(自然科学版),2013,(06)：2267-2273.
[94]JUTTEN C, HERAULT J. ADAPTIVE DISCRIMINATION OF SOURCES FROM AN ARRAY OF SENSORS; proceedings of the 7th European Conference on Electrotechnics:Advanced Technologies and Processes in Communication and Power Systems-EUROCON 86, Paris, Fr, F,1986 [C]. Comite EUROCON 86.
[95]张洪渊,史习智.一种任意信号源盲分离的高效算法[J].电子学报,2001,(10)：1392-1396.
[96]COMON P, JUTTEN C, HERAULT J. Blind separation of sources, part II. Problems statement [J]. Signal Processing,1991,24 (1):11-20.
[97]NAIK G R, KUMAR D K, WEGHORN H. Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG; proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, December 3,2007-December 6, 2007, Melbourne, VIC, Australia, F,2007 [C]. Inst. of Elec. and Elec. Eng. Computer Society.
[98]JBARI A, ADIB A, ABOUTAJDINE D. Blind source separation based on wavelet signal representation; proceedings of the 14th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2007, December 11,2007-December 14,2007, Marrakech, Morocco, F,2007 [C]. Institute of Electrical and Electronics Engineers Inc.
[99]肖文书,张兴敢.基于盲源分离算法的阵列信号波达方向-频率估计[J].南京大学学报(自然科学版),2009,(04)：463-472.
[100]LU Y, WANG F, LUO X, et al. An ICA-based image fusion scheme using only source images; proceedings of the 2nd International Conference of Electrical and Electronics Engineering, ICEEE 2011, December 1,2011-December 2,2011, Macau, China, F,2012 [C]. Springer Verlag.
[101]LIAO L D, HE Q H, HU Z L. Blind separation of excavator noise signals in frequency domain; proceedings of the 2011 International Conference on Vibration, Structural Engineering and Measurement, ICVSEM2011, October 21, 2011-October 23,2011, Shanghai, China, F,2012 [C]. Trans Tech Publications.
[102]SERVIERE C, FABRY P. Blind source separation of noisy harmonic signals for rotating machine diagnosis [J]. Journal of Sound and Vibration,2004,272 (1-2): 317-339.
[103]TACHIBANA K, SARUWATARI H, MORI Y, et al. Efficient blind source separation combining closed-form second-order ICA and nonclosed-form higher-order ICA; proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'07, April 15,2007-April 20, 2007, Honolulu, HI, United states, F,2007 [C]. Institute of Electrical and Electronics Engineers Inc.
[104]WU X, HE J, JIN S, et al. Blind separation of speech signals based on wavelet transform and independent component analysis [J]. Transactions of Tianjin University,2010,16(2):123-128.
[105]WANG D, TSE P W. A new blind fault component separation algorithm for a single-channel mechanical signal mixture [J].2012,
[106]GUO Y, HUANG S, LI Y. Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis [J]. Circuits, Systems, and Signal Processing,2012,31 (6): 2047-2060.
[107]GUO Y, HUANG S, LI Y, et al. Edge effect elimination in single-mixture blind source separation [J]. Circuits, Systems, and Signal Processing,2013,32 (5): 2317-2334.
[108]HYVARINEN A, HURRI J. Blind separation of sources that have spatiotemporal variance dependencies, F,2004 [C]. Elsevier.
[109]JAIN V K, BHANJA S, CHAPMAN G H, et al. A parallel architecture for the ICA algorithm:DSP plane of a 3-D heterogeneous sensor; proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'05, March 18,2005-March 23,2005, Philadelphia, PA, United states, F, 2005 [C]. Institute of Electrical and Electronics Engineers Inc.
[110]CHO J, YOO C D. A maximum likelihood approach for underdetermined TDOA estimation; proceedings of the 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, May 26,2013-May 31, 2013, Vancouver, BC, Canada, F,2013 [C]. Institute of Electrical and Electronics Engineers Inc.
[111]HAO Q, LI S, SHEN H, et al. Blind separation for cabin acoustic signals in complex environment; proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09, October 17,2009-October 19,2009, Tianjin, China, F,2009 [C]. IEEE Computer Society.
[112]DE-LA-ROSA J-J G, PUNTONET C G, MUNOZ A M, et al. An application of ICA to BSS in a container gantry crane cabin's model; proceedings of the 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, September 9,2007-September 12,2007, London, United kingdom, F,2007 [C]. Springer Verlag.
[113]HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences,1998,454 (1971):903-995.
[114]ZHAOHUA W, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, Theory and Applications,2009,1 (1):1-41.
[115]WU Z, HUANG N E, CHEN X. The multi-dimensional ensemble empirical mode decomposition method [J]. Advances in Adaptive Data Analysis,2009,1 (3): 339-372.
[116]YEH J-R, SHIEH J-S, HUANG N E. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method [J]. Advances in Adaptive Data Analysis,2010,2 (2):135-156.
[117]郑旭,郝志勇,金阳,等.基于eemd与广义s变换的内燃机噪声源识别研究[J].内燃机工程,2011,(05)：68-73.
[118]KAMATH V, LAI Y-C, ZHU L, et al. Empirical mode decomposition and blind source separation methods for antijamming with GPS signals; proceedings of the 2006 IEEE/ION Position, Location, and Navigation Symposium, April 25,2006-April 27,2006, San Diego, CA, United states, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[119]MU Y, YAN S, HUANG T, et al. Contextual motion field-based distance for video analysis, Tiergartenstrasse 17, Heidelberg, D-69121, Germany, F,2008 [C]. Springer Verlag.
[120]HE Q, DU R. Mechanical watch signature analysis based on wavelet decomposition [J]. International Journal of Wavelets, Multiresolution and Information Processing,2009,7 (4):491-512.
[121]NUNEZ P, DREWS JR P, ROCHA R, et al. Novelty detection and 3D shape retrieval based on Gaussian mixture models for autonomous surveillance robotics; proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, October 11,2009-October 15,2009, St. Louis, MO, United states, F,2009 [C]. IEEE Computer Society.
[122]LI X-F, LIU M-J, WANG S-H. Research on the EEMD algorithm of penetration acceleration signal processing based on independent component analysis; proceedings of the 2010 3rd International Congress on Image and Signal Processing, CISP 2010, October 16,2010-October 18,2010, Yantai, China, F, 2010 [C]. IEEE Computer Society.
[123]GUO Y, NAIK G R, NGUYEN H. Single channel blind source separation based local mean decomposition for Biomedical applications; proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, July 3,2013-July 7,2013, Osaka, Japan, F,2013 [C]. Institute of Electrical and Electronics Engineers Inc.
[124]李小兵,初孟,邱天爽,等.一种基于经验模态分解的时频分布及其在eeg分析中的应用[J].生物医学工程学杂志,2007,(05)：990-995.
[125]TAELMAN J, MIJOVIC B, VAN HUFFEL S, et al. ECG artifact removal from surface EMG signals by combining empirical mode decomposition and independent component analysis; proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2011, January 26, 2011-January 29,2011, Rome, Italy, F,2011 [C]. Inst. for Syst. and Technol. of Inf. Control and Commun.
[126]SAFIEDDINE D, KACHENOURA A, ALBERA L, et al. Removal of muscle artifact from EEG data:Comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches [J]. Eurasip Journal on Advances in Signal Processing,2012,2012 (1):
[127]ZENG H, SONG A, YAN R, et al. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition [J]. Sensors (Switzerland),2013,13 (11):14839-14859.
[128]DYBAA J, ZIMROZ R. Empirical mode decomposition of vibration signal for detection of local disturbances in planetary gearbox used in heavy machinery system; proceedings of the 5th International Congress of Technical Diagnostics, September 3,2012-September 5,2012, Krakow, Poland, F,2014 [C]. Trans Tech Publications Ltd.
[129]MOLLAM K I, HIROSE K, MINEMATSU N. Separation of mixed audio signals by source localization and binary masking with Hilbert spectrum; proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006, March 5,2006-March 8,2006, Charleston, SC, United states, F,2006 [C]. Springer Verlag.
[130]MOLLA M K I, HIROSE K, MINEMATSU N. Localization based separation of mixed audio signals with binary masking of hilbert spectrum; proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, May 14,2006-May 19,2006, Toulouse, France, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[131]MOLLAR M K I, HIROSE K, MINEMATSU N. Localization based audio source separation by sub-band beamforming; proceedings of the ISC AS 2006:2006 IEEE International Symposium on Circuits and Systems, May 21,2006-May 24, 2006, Kos, Greece, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[132]CAI Y-P, LI A-H, WANG T, et al. I.C. engine vibration time-frequency analysis based on EMD-Wigner-Ville [J]. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering,2010,23 (4):430-437.
[133]TAKANO Y, YAMAMOTO Y. Metric-preservtng reduction of earth mover's distance [J]. Asia-Pacific Journal of Operational Research,2010,27 (1):39-54.
[134]WARBHE A D, DHARASKAR R V, KALAMBHE B. A single channel phonocardiograph processing using EMD, SVD, and EFICA; proceedings of the 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010, November 19,2010-November 21,2010, Goa, India, F,2010 [C]. IEEE Computer Society.
[135]YE H, YANG S, YANG J. Mechanical vibration source number estimation based on EMD-SVD-BIC [J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,2010,30 (3):330-334.
[136]JIANG Y, QIN L, ZHANG Y, et al. Vibration signal processing for gear fault diagnosis based on empirical mode decomposition and nonlinear blind source separation,5 Wates Way, Brentwood, Essex, CM15 9TB, United Kingdom, F, 2011 [C]. Multi-Science Publishing Co. Ltd.
[137]HAO Z, MA Z, ZHOU H. Research on fault diagnosis method based on empirical mode decomposition time-frequency reassignment; proceedings of the 2011 International Conference on Material Science and Information Technology, MSIT2011, September 16,2011-September 18,2011, Singapore, Singapore, F, 2012 [C]. Trans Tech Publications.
[138]LI Q, FU C, JIANG H, et al. Analysis of blind source separation for single channel vibration signal decomposition with a composite method [J]. International Journal of Digital Content Technology and its Applications,2012,6 (22):610-620.
[139]CAO W, FU P, XU G. Fault diagnosis of tool wear based on weak feature extraction and GA-B-spline network [J]. Sensors and Transducers,2013,152 (5): 60-67.
[140]WANG J, GAO R X, YAN R. Integration of EEMD and ICA for wind turbine gearbox diagnosis [J].2013,
[141]WANG Y, REN X, YANG Y, et al. An efficient denoising source separation (DSS) of rotating machine fault signals based on empirical mode decomposition (EMD) [J]. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University,2013,31 (2):272-276.
[142]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,(04)：12-16.
[143]BRONSTEIN M M, BRONSTEIN A M, ZIBULEVSKY M, et al. Reconstruction in diffraction ultrasound tomography using nonuniform FFT [J]. IEEE Transactions on Medical Imaging,2002,21 (11):1395-1401.
[144]BRONSTEIN A M, BRONSTEIN M M, ZIBULEVSKY M, et al. Separation of reflections via sparse ICA; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F,2003 [C]. SPIE.
[145]KISILEV P, ZIBULEVSKY M, ZEEVI Y Y. Application of wavelets in blind source separation; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F, 2003 [C]. SPIE.
[146]ZIBULEVSKY M. Blind Separation of Sparse Sources with Relative Newton Method; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F, 2003 [C]. SPIE.
[147]左翠鹏.液压挖掘机驾驶室噪声控制研究[D].硕士学位论文：山东大学,2013.
[148]廖力达.挖掘机用柴油机噪声声源识别与特性研究[D].博士学位论文：中南大学,2012.
[149]窦青青.基于小波时频分析的工程机械驾驶室噪声源识别研究[D].硕士学位论文：山东大学,2012.
[150]郑旭.车辆与内燃机振声信号盲分离及噪声源识别的研究[D].博士学位论文：浙江大学,2012.
[151]HUANG N E, WU Z. ENSEMBLE EMPIRICAL MODE DECOMPOSITION:A NOISE-ASSISTED DATA ANALYSIS METHOD [J]. Advances in Adaptive Data Analysis,2009,01 (01):1-41.
[152]刘佳,杨士莪,朴胜春.基于eemd的地声信号单通道盲源分离算法[J].哈尔滨工程大学学报,2011,(02)：194-199.
[153]郑旭,郝志勇,金阳,等.采用改进的集总平均经验模态分解法的内燃机气门拍击激励与燃烧激励分离的研究[J].汽车工程,2011,(11)：930-936.
[154]周晓峰.机械振动源的分离和识别方法研究[D].博士学位论文：浙江大学,2012.
[155]信子君.通信信号与干扰信号的半盲分离算法研究[D].硕士学位论文：电子科技大学,2013.
[156]熊忻,杨世锡,周晓峰.旋转机械振动信号的固有模式函数降噪方法[J].浙江大学学报(工学版),2011,(08)：1376-1381.
[157]高峰.欠定松弛稀疏信号的盲分离研究[D].博士学位论文：华南理工大学,2012.
[158]叶红仙.机械系统振动源的盲分离方法研究[D].博士学位论文：浙江大学,2008.
[159]叶红仙,杨世锡,杨将新.基于EMD-SVD-BIC的机械振动源数估计方法[J].振动测试与诊断,2010,(03)：330-334+343.
[160]ANTONI J, GUILLET F, EL BADAOUI M, et al. Blind separation of convolved cyclostationary processes [J]. Signal Processing,2005,85 (1):51-66.
[161]张之猛.水声信号处理中的盲解卷积技术研究[D].博士学位论文：哈尔滨工程大学,2009.
[162]高鹰,谢胜利.基于信号稀疏特性和核函数的非线性盲信号分离算法[J].计算机工程与应用,2005,(01)：33-35.
[163]路亮,龙源,钟明寿,et al. FastICA算法在低信噪比爆破振动信号信噪分离中的应用研究[J].振动与冲击,2012,(17)：33-37.
[164]HYVARINEN A. Family of fixed-point algorithms for independent component analysis; proceedings of the Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP Part 1 (of 5), April 21,1997-April 24,1997, Munich, Ger, F,1997 [C]. IEEE.
[165]白树忠.欠定盲源分离算法及在语音处理中的应用研究[D].博士学位论文：山东大学,2008.
[166]HYVARINEN A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Transactions on Neural Networks,1999,10 (3): 626-634.
[167]李晶皎,安冬,王骄.基于eemd和ica的语音去噪方法[J].东北大学学报(自然科学版),2011,(11)：1554-1557.
[2]SUZUKI Y, FUJII Y, WATARI A. REDUCTION OF INTERIOR CAR NOISE BY USING VECTOR METHOD [J].1978, v (3):1447-1462.
[3]KIM J, VARADAN V V, KO B. Finite element modeling of active cabin noise control problems; proceedings of the Smart Materials, Structures, and MEMS, December 11,1996-December 14,1996, Bangalore, India, F,1996 [C]. SPIE.
[4]MORRISON H B, CASALI J G. Intelligibility of synthesized voice messages in commercial truck cab noise for normal-hearing and hearing-impaired listeners [J]. International Journal of Speech Technology,1997,2 (1):33-44.
[5]KIM J, KO B, LEE J, et al. Optimal design of piezoelectric smart structures for active cabin noise control; proceedings of the Smart Structures and Materials 1998 Mathematics and Control in Smart Structures, May 2,1998-May 5,1998, San Diego, CA, United states, F,1998 [C]. SPIE.
[6]KIM J, KO B, LEE J-K, et al. Finite element modeling of a piezoelectric smart structure for the cabin noise problem [J]. Smart Materials and Structures,1999,8 (3):380-389.
[7]GOPINATHAN S V, VARADAN V V, VARADAN V K. Finite element/boundary element simulation of interior noise control using active-passive control technique [M]//VARADAN V V. Smart Structures and Materials 2000:Mathematics and Control in Smart Structures.2000:22-32.
[8]LEE D H, HWANG W S, KIM C M. Design sensitivity analysis and optimization of an engine mount system using an FRF-based substructuring method [J]. Journal of Sound and Vibration,2002,255 (2):383-397.
[9]SONG C K, HWANG J K, LEE J M, et al. Active vibration control for structural-acoustic coupling system of a 3-D vehicle cabin model [J]. Journal of Sound and Vibration,2003,267 (4):851-865.
[10]DIAZ J, EGANA J M, VINOLAS J. A local active noise control system based on a virtual-microphone technique for railway sleeping vehicle applications [J]. Mechanical Systems and Signal Processing,2006,20 (8):2259-2276.
[11]THOMAS J K, LOVSTEDT S, BLOTTERS J D, et al. Eigenvalue equalization filtered-x (EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin [J]. Noise Control Engineering Journal,2008,56 (1):25-34.
[12]LALOR N, PRLEBSCH H H. The prediction of low-and mid-frequency internal road vehicle noise:A literature survey [J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2007,221 (3): 245-269.
[13]KAWANO J, AMAKASU J, TANAKA T. Noise detection technology development for car cabin; proceedings of the 2008 World Congress, April 14,2008-April 17, 2008, Detroit, MI, United states, F,2008 [C]. SAE International.
[14]HOSSEINI FOULADIM, NOR M J M, ARIFFIN A K. Spectral analysis methods for vehicle interior vibro-acoustics identification [J]. Mechanical Systems and Signal Processing,2009,23 (2):489-500.
[15]KERKETTA S, BAGH S. Noise levels inside the cabin of heavy earth moving machineries:Its significance and association [J]. Noise and Vibration Worldwide, 2011,42(5):14-19.
[16]ALI M E H R, ATTARI M A. Design and implementation of ANC algorithm for engine noise reduction inside an automotive cabin using TMS320C5510; proceedings of the 2011 19th Iranian Conference on Electrical Engineering, ICEE 2011, May 17,2011-May 19,2011, Tehran, Iran, F,2011 [C]. IEEE Computer Society.
[17]YUKSEL E, KAMCI G, BASDOGAN I. Vibro-acoustic design optimization study to improve the sound pressure level inside the passenger Cabin [J]. Journal of Vibration and Acoustics, Transactions of the ASME,2012,134 (6).
[18]MUHAMAD W Z A W, JUNOH A K. Optimization of noise and vibration in passenger car cabin; proceedings of the 2012 IEEE Colloquium on Humanities, Science and Engineering Research, CHUSER 2012, December 3,2012 December 4,2012, Kota Kinabalu, Sabah, Malaysia, F,2012 [C]. IEEE Computer Society.
[19]AIRAKSINEN T, TOIVANEN J. An optimal local active noise control method based on stochastic finite element models [J]. Journal of Sound and Vibration, 2013,332 (26):6924-6933.
[20]KULAH S, ARIDOGAN U, BASDOGAN I. Investigation of an Active Structural Acoustic Control System on a Complex 3D Structure; proceedings of the 31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013, February 11,2013-February 14,2013, Garden Grove, CA, United states, F,2014 [C]. Springer New York.
[21]GOODWIN D W. Low frequency noise in motor cars-study of origins and design parameters [D]. Ph.D. Thesis:University of Southampton,1968.
[22]JHA S K. CHARACTERISTICS AND SOURCES OF NOISE AND VIBRATION AND THEIR CONTROL IN MOTOR CARS [J]. Journal of Sound and Vibration, 1976,48 (4):543-558.
[23]蔡应强,陈清林.国内外推土机的发展现状及趋势[J].机床与液压,2013,(08)：133-136.
[24]WU J-D, LEE T-H, BAI M R. Background noise cancellation for hands-free communication system of car cabin using adaptive feedforward algorithms [J]. International Journal of Vehicle Design,2003,31 (4):440-451.
[25]WILLINGALE B J. LOW FREQUENCY SOUND IN LOCOMOTIVE DRIVING CABINS; proceedings of the 10th International Congress on Acoustics Volume 3: Contributed Papers Continued, Sydney, Aust, F,1980 [C].
[26]WANG L, GAN W S, CHONG Y K, et al. A novel approach to bass enhancement in automobile cabin; proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, May 14,2006-May 19, 2006, Toulouse, France, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[27]WANG C Y, VAICAITIS R. Active control of vibrations and noise of double wall cylindrical shells [J]. Journal of Sound and Vibration,1998,216 865-888.
[28]VENEGAS R, NABUCO M, MASSARANI P. Sound insulation evaluation using transfer function measurements; proceedings of the 34th International Congress on Noise Control Engineering 2005, INTERNOISE 2005, August 7,2005- August 10,2005, Rio de Janeiro, Brazil, F,2005 [C]. Institute of Noise Control Engineering of the USA.
[29]UTSUNO H, TANAKA T. Reduction of acoustic standing waves in construction machine cabins by tuned vinyl covered urethane absorbing materials [J]. Proceedings-International Conference on Noise Control Engineering,1988, 1467-1467.
[30]NAKKULA JR W, ZIMMERLI R, BOROUGH R. An example of noise control treatment for construction Machinery Cab Interiors; proceedings of the Earthmoving Industry Conference, April 6,1981-April 8,1981, Peoria, IL, United states, F,1981 [C]. SAE International.
[31]TANAKA T, UTSUNO H, TARUYAMA K, et al. Techniques to control construction machinery noise [J]. R and D:Research and Development Kobe Steel Engineering Reports,1988,38 (3):6-9.
[32]DESMET W, PLUYMERS B, SAS P. Vibro-acoustic analysis procedures for the evaluation of the sound insulation characteristics of agricultural machinery cabins [J]. Journal of Sound and Vibration,2003,266 (3):407-441.
[33]GUJARATHI R N, KOLTE K S, KSHIRSAGAR K G, et al. Panel contribution study of a commercial excavator cab using sound intensity measurements; proceedings of the 22nd National Conference on Noise Control Engineering, NOISE-CON 2007, October 22,2007-October 24,2007, Reno, NV, United states, F,2007 [C]. Institute of Noise Control Engineering of the USA.
[34]SHIN C W, PARK S Y, KANG Y J, et al. Interior noise reduction of wheel loader using Boundary Element Method; proceedings of the 15th International Congress on Sound and Vibration 2008, ICSV 2008, July 6,2008-July 10,2008, Daejeon, Korea, Republic of, F,2008 [C]. International Institute of Acoustics and Vibrations.
[35]WILLEMSEN A M, PORADEK F, RAO M D. Reduction of noise in an excavator cabin using order tracking and ultrasonic leak detection [J]. Noise Control Engineering Journal,2009,57 (5):400-412.
[36]KIM K T, KIM H T, JOO W H. Low noise cabin design for construction equipment; proceedings of the SAE 2013 Noise and Vibration Conference and Exhibition, NVC 2013, May 20,2013-May 23,2013, Grand Rapids, MI, United states, F, 2013 [C]. SAE International.
[37]马燕,陈剑,袁正.矿用自卸汽车驾驶室噪声源识别[J].噪声与振动控制,2012,(03)：118-120+132.
[38]姜义林,李世伟,刘建美.隔振器在工程机械驾驶室噪声控制中的应用[J].山东理工大学学报(自然科学版),2005,(05)：73-76.
[39]张瑞.工程机械驾驶室新材料与新工艺的应用[J].工程机械,2012,(04)：58-63+59.
[40]张立军周余靳.发动机振动引起的车内噪声控制研究[J].振动、测试与诊断,2001,(01)：61-66+76.
[41]于坚.传动轴的振动及驾驶室噪声[J].汽车技术,1988,(02)：12-15.
[42]徐心虹.装载机驾驶室的噪声控制[J].工程机械,1991,(09)：24-27+51.
[43]陆森林,宫镇.拖拉机驾驶室内噪声分析[J].江苏工学院学报,1990,(03)：43-50.
[44]封磊,方玉莹,左言言.某叉车驾驶室外辐射声场预测研究[J].科学技术与工程,2013,(25)：7422-7426.
[45]常振臣王周郭.车内噪声控制技术研究现状及展望[J].吉林工业大学学报(工学版),2002,(04)：86-90.
[46]丁玉兰.工程机械的重要研究课题——机械噪声[J].工程机械,1981,(06)：52-54.
[47]蔡世彦.国内外工程机械司机室现状分析[J].工程机械,1988,(06)：41-45+11+54.
[48]朱桂华,宫镇,胡均平,等.统计能量分析用于工程机械驾驶室噪声预估[J].中南工业大学学报(自然科学版),2003,(02)：166-169.
[49]邓习树,邵威,黄志亮.工程机械驾驶室内部噪声预估分析[J].中国工程机械学报,2012,(04)：458-462.
[50]蒋丰鑫,陈剑,肖悦.挖掘机驾驶室声-固耦合系统低频噪声分析[J].工程机械,2013,(08)：25-30+42.
[51]易小刚,邓习树.工程机械驾驶室内部噪声源识别的小波分析方法[J].中国工程机械学报,2009,(03)：265-269.
[52]金岩,常志权.频谱分析技术在车辆nvh故障诊断中的应用[J].噪声与振动控制,2011,(01)：110-113.
[53]陈心昭.噪声源识别技术的进展[J].合肥工业大学学报(自然科学版),2009,(05)：609-614.
[54]MAYNARD J D, WILLIAMS E G, LEE Y. Nearfield acoustic holography. I. Theory of generalized holography and the development of NAH [J]. Journal of the Acoustical Society of America,1985,78 (4):1395-1413.
[55]WILLIAMS E G, DARDY H D, WASHBURN K B. Generalized nearfield acoustical holography for cylindrical geometry:theory and experiment [J]. Journal of the Acoustical Society of America,1987,81 (2):389-407.
[56]XIANG C-L, CHEN F-Z, LIU H, et al. Experimental research on noise source identification and the radiation characteristics of a gearbox based on NAH [J]. Binggong Xuebao/Acta Armamentarii,2009,30 (11):1424-1429.
[57]ZHOU L, DING S, ZHANG R, et al. Effect on measurement of the radiated acoustic field of underwater structure based on NAH with swinging measuring surface; proceedings of the International Conference on Mechanical Science and Engineering, ICMSE 2012, July 20,2012-July 22,2012, Beijing, China, F,2012 [C]. Trans Tech Publications.
[58]ZHANG D, ZHU N, CHENG J, et al. Experimental research on vibrating objects and its radiation field using near-field acoustic holography [J]. Shengxue Xuebao/Acta Acustica,1995,20 (4):250-255.
[59]WILLIAMS E G, HOUSTON B H, HERDIC P C. Reconstruction of the surface velocity and interior acoustic intensity from an aircraft fuselage using nearfield acoustical holography; proceedings of the Proceedings of the 1996 National Conference on Noise Control Engineering Part 2 (of 2), September 29,1996-October 2,1996, Bellevue, WA, USA, F,1996 [C]. Inst of Noise Control Engineering.
[60]WANG X-Q, YANG S-F, ZHAO J-M, et al. Application research on nah for identification of plant sound source; proceedings of the 2008 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2008, December 5,2008-December 8,2008, Nanjing, China, F,2008 [C]. Inst. of Elec. and Elec. Eng. Computer Society.
[61]WAN Q, JIANG W K. Near field acoustic holography (NAH) theory for cyclostationary sound field and its application [J]. Journal of Sound and Vibration, 2006,290 (3-5):956-967.
[62]TAKATA H, HALLMAN D, BOLTON J S. Research on nearfield acoustic holography (Selection strategies for optimal reference sets using singular value decomposition) [J]. Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C,1995,61 (584):1503-1508.
[63]SUN X-W, CHEN Q-F, CHEN X-R, et al. Ab initio study of phase transition and bulk modulus of NaH [J]. Journal of Solid State Chemistry,2011,184 (2): 427-431.
[64]IH J-G, KIM B-K. On the use of the BEM-based NAH for the vibro-acoustic source imaging on the nonregular exterior surfaces; proceedings of the Proceedings of the 1998 National Conference on Noise Control Engineering Part 1 (of 3), April 5,1998-April 9,1998, Ypsilanti, MI, USA, F,1998 [C]. Inst Noise Control Eng.
[65]HALD J. Combined NAH and beamforming using the same microphone array [J]. Sound and Vibration,2004,38 (12):18-27.
[66]DUMBACHER S M, BROWN D L, BLOUGH J R, et al. Practical aspects of making NAH measurements; proceedings of the Noise and Vibration Conference and Exposition, May 17,1999-May 20,1999, Traverse City, MI, United states, F, 1999 [C]. SAE International.
[67]BHATTACHARJEE A, DASTIDAR K R. Control of de-excitation to selected vibrational levels in the ground state of NaH molecule using two broadband ultrashort pulses [J]. Molecular Physics,2006,104 (15):2485-2495.
[68]LI Z, FENG T, HUANG Z, et al. The noise source localization of industrial sewing machine by NAH method; proceedings of the 2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012, December 8,2012-December 9,2012, Taiwan, F,2013 [C]. Trans Tech Publications.
[69]褚志刚,杨洋,倪计民,等.波束形成声源识别技术研究进展[J].声学技术,2013,(05)：430-435.
[70]褚志刚,蔡鹏飞,蒋忠翰,等.基于声阵列技术的柴油机噪声源识别[J].农业工程学报,2014,(02)：23-30.
[71]杨洋,倪计民,褚志刚,等.基于波束形成的发动机噪声源识别及声功率计算[J].内燃机工程,2013,(03)：39-43+49.
[72]周卫东,贾磊.小波变换和独立分量分析去除脑电信号中的噪声和干扰[J].山东大学学报(医学版),2003,(02)：116-119+122.
[73]钟飞,谭中军,史铁林,等.基于ica和小波变换的轴承故障特征提取[J].微计算机信息,2007,(28)：154-155+269.
[74]李秀坤,李婷婷,夏峙.水下目标特性特征提取及其融合[J].哈尔滨工程大学学报,2010,(07)：903-908.
[75]胡伊贤,李舜酩,张袁元,等.车辆噪声源识别方法综述[J].噪声与振动控制,2012,(05)：11-15+20.
[76]张俊红,李林洁,刘海,等.基于经验模态分解和独立成分分析的柴油机噪声源识别技术[J].内燃机学报,2012,(06)：544-549.
[77]张波,毕传兴,徐亮.基于人工神经网络模型的车门关闭声声品质评价方法研究[J].汽车工程,2011,(11)：1003-1006.
[78]王保平,王增才,张万枝.基于emd与神经网络的煤岩界面识别方法[J].振动测试与诊断,2012,(04)：586-590+688.
[79]杨健,李颖晖,熊大顺.基于小波包能量谱和改进神经网络的异步电机故障诊断[J].电力电子,2013,(02)：19-21+31.
[80]刘冰,张梅.车内噪声源识别技术的研究[J].机械管理开发,2013,(03)：13-15.
[81]刘建敏,李晓磊,乔新勇,等.基于emd和stft柴油机缸盖振动信号时频分析[J].噪声与振动控制,2013,(02)：133-137.
[82]赵志宏,杨绍普,申永军.基于独立分量分析与相关系数的机械故障特征提取[J].振动与冲击,2013,(06)：67-72.
[83]HAMMOND J K, WHITE P R. Analysis of non-stationary signals using time-frequency methods [J]. Journal of Sound and Vibration,1996,190 (3): 419-447.
[84]董建华,顾汉明,张星.几种时频分析方法的比较及应用[J].工程地球物理学报,2007,(04)：312-316.
[85]邹红星,周小波,李衍达.时频分析：回溯与前瞻[J].电子学报,2000,(09)：78-84.
[86]郭奇,刘卜瑜,史立波,等.基于二次EEMD的Wigner-Ville分布旋转机械故障信号分析及试验研究[J].振动与冲击,2012,(13)：129-133+153.
[87]宁静,诸昌钤,张兵.基于EMD和Cohen核的轨道不平顺信号分析方法[J].振动与冲击,2013,(04)：31-38.
[88]UCHIDA H, UEDA K. Detection of transient noise of car interior using non-stationary signal analysis; proceedings of the 1998 SAE International Congress and Exposition, February 23,1998-February 26,1998, Detroit, MI, United states, F,1998 [C]. SAE International.
[89]ISHIMITSU S, KOBAYASHI H. Study on instantaneous correlation analyses of acceleration car interior noise using wavelets and its subjective evaluation [J]. Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C,2006,72 (7):2094-2100.
[90]ISHIMITSU S, TAKAMI K, SAKAMOTO K, et al. Study on the contribution of intake noise using complex time-time analysis and subjective evaluation [J]. International Journal of Wavelets, Multiresolution and Information Processing, 2010,8 (4):609-625.
[91]RUBIO E, JAUREGUI J C. Experimental characterization of mechanical vibrations and acoustical noise generated by defective automotive wheel hub bearings [M]//ZUPPA L A, COVARRUBIAS R D H, ANDRES M V, et al. International Meeting of Electrical Engineering Research 2012.2012:176-181.
[92]余烽,徐中明,周小林,等.基于复Morlet小波的混合动力客车噪声识别与改进[J].机械传动,2013,(07)：88-90+93.
[93]黄志雄,何清华.液压挖掘机反铲切削过程振动信号去噪处理[J].中南大学学报(自然科学版),2013,(06)：2267-2273.
[94]JUTTEN C, HERAULT J. ADAPTIVE DISCRIMINATION OF SOURCES FROM AN ARRAY OF SENSORS; proceedings of the 7th European Conference on Electrotechnics:Advanced Technologies and Processes in Communication and Power Systems-EUROCON 86, Paris, Fr, F,1986 [C]. Comite EUROCON 86.
[95]张洪渊,史习智.一种任意信号源盲分离的高效算法[J].电子学报,2001,(10)：1392-1396.
[96]COMON P, JUTTEN C, HERAULT J. Blind separation of sources, part II. Problems statement [J]. Signal Processing,1991,24 (1):11-20.
[97]NAIK G R, KUMAR D K, WEGHORN H. Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG; proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, December 3,2007-December 6, 2007, Melbourne, VIC, Australia, F,2007 [C]. Inst. of Elec. and Elec. Eng. Computer Society.
[98]JBARI A, ADIB A, ABOUTAJDINE D. Blind source separation based on wavelet signal representation; proceedings of the 14th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2007, December 11,2007-December 14,2007, Marrakech, Morocco, F,2007 [C]. Institute of Electrical and Electronics Engineers Inc.
[99]肖文书,张兴敢.基于盲源分离算法的阵列信号波达方向-频率估计[J].南京大学学报(自然科学版),2009,(04)：463-472.
[100]LU Y, WANG F, LUO X, et al. An ICA-based image fusion scheme using only source images; proceedings of the 2nd International Conference of Electrical and Electronics Engineering, ICEEE 2011, December 1,2011-December 2,2011, Macau, China, F,2012 [C]. Springer Verlag.
[101]LIAO L D, HE Q H, HU Z L. Blind separation of excavator noise signals in frequency domain; proceedings of the 2011 International Conference on Vibration, Structural Engineering and Measurement, ICVSEM2011, October 21, 2011-October 23,2011, Shanghai, China, F,2012 [C]. Trans Tech Publications.
[102]SERVIERE C, FABRY P. Blind source separation of noisy harmonic signals for rotating machine diagnosis [J]. Journal of Sound and Vibration,2004,272 (1-2): 317-339.
[103]TACHIBANA K, SARUWATARI H, MORI Y, et al. Efficient blind source separation combining closed-form second-order ICA and nonclosed-form higher-order ICA; proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'07, April 15,2007-April 20, 2007, Honolulu, HI, United states, F,2007 [C]. Institute of Electrical and Electronics Engineers Inc.
[104]WU X, HE J, JIN S, et al. Blind separation of speech signals based on wavelet transform and independent component analysis [J]. Transactions of Tianjin University,2010,16(2):123-128.
[105]WANG D, TSE P W. A new blind fault component separation algorithm for a single-channel mechanical signal mixture [J].2012,
[106]GUO Y, HUANG S, LI Y. Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis [J]. Circuits, Systems, and Signal Processing,2012,31 (6): 2047-2060.
[107]GUO Y, HUANG S, LI Y, et al. Edge effect elimination in single-mixture blind source separation [J]. Circuits, Systems, and Signal Processing,2013,32 (5): 2317-2334.
[108]HYVARINEN A, HURRI J. Blind separation of sources that have spatiotemporal variance dependencies, F,2004 [C]. Elsevier.
[109]JAIN V K, BHANJA S, CHAPMAN G H, et al. A parallel architecture for the ICA algorithm:DSP plane of a 3-D heterogeneous sensor; proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'05, March 18,2005-March 23,2005, Philadelphia, PA, United states, F, 2005 [C]. Institute of Electrical and Electronics Engineers Inc.
[110]CHO J, YOO C D. A maximum likelihood approach for underdetermined TDOA estimation; proceedings of the 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, May 26,2013-May 31, 2013, Vancouver, BC, Canada, F,2013 [C]. Institute of Electrical and Electronics Engineers Inc.
[111]HAO Q, LI S, SHEN H, et al. Blind separation for cabin acoustic signals in complex environment; proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09, October 17,2009-October 19,2009, Tianjin, China, F,2009 [C]. IEEE Computer Society.
[112]DE-LA-ROSA J-J G, PUNTONET C G, MUNOZ A M, et al. An application of ICA to BSS in a container gantry crane cabin's model; proceedings of the 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, September 9,2007-September 12,2007, London, United kingdom, F,2007 [C]. Springer Verlag.
[113]HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences,1998,454 (1971):903-995.
[114]ZHAOHUA W, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, Theory and Applications,2009,1 (1):1-41.
[115]WU Z, HUANG N E, CHEN X. The multi-dimensional ensemble empirical mode decomposition method [J]. Advances in Adaptive Data Analysis,2009,1 (3): 339-372.
[116]YEH J-R, SHIEH J-S, HUANG N E. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method [J]. Advances in Adaptive Data Analysis,2010,2 (2):135-156.
[117]郑旭,郝志勇,金阳,等.基于eemd与广义s变换的内燃机噪声源识别研究[J].内燃机工程,2011,(05)：68-73.
[118]KAMATH V, LAI Y-C, ZHU L, et al. Empirical mode decomposition and blind source separation methods for antijamming with GPS signals; proceedings of the 2006 IEEE/ION Position, Location, and Navigation Symposium, April 25,2006-April 27,2006, San Diego, CA, United states, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[119]MU Y, YAN S, HUANG T, et al. Contextual motion field-based distance for video analysis, Tiergartenstrasse 17, Heidelberg, D-69121, Germany, F,2008 [C]. Springer Verlag.
[120]HE Q, DU R. Mechanical watch signature analysis based on wavelet decomposition [J]. International Journal of Wavelets, Multiresolution and Information Processing,2009,7 (4):491-512.
[121]NUNEZ P, DREWS JR P, ROCHA R, et al. Novelty detection and 3D shape retrieval based on Gaussian mixture models for autonomous surveillance robotics; proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, October 11,2009-October 15,2009, St. Louis, MO, United states, F,2009 [C]. IEEE Computer Society.
[122]LI X-F, LIU M-J, WANG S-H. Research on the EEMD algorithm of penetration acceleration signal processing based on independent component analysis; proceedings of the 2010 3rd International Congress on Image and Signal Processing, CISP 2010, October 16,2010-October 18,2010, Yantai, China, F, 2010 [C]. IEEE Computer Society.
[123]GUO Y, NAIK G R, NGUYEN H. Single channel blind source separation based local mean decomposition for Biomedical applications; proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, July 3,2013-July 7,2013, Osaka, Japan, F,2013 [C]. Institute of Electrical and Electronics Engineers Inc.
[124]李小兵,初孟,邱天爽,等.一种基于经验模态分解的时频分布及其在eeg分析中的应用[J].生物医学工程学杂志,2007,(05)：990-995.
[125]TAELMAN J, MIJOVIC B, VAN HUFFEL S, et al. ECG artifact removal from surface EMG signals by combining empirical mode decomposition and independent component analysis; proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2011, January 26, 2011-January 29,2011, Rome, Italy, F,2011 [C]. Inst. for Syst. and Technol. of Inf. Control and Commun.
[126]SAFIEDDINE D, KACHENOURA A, ALBERA L, et al. Removal of muscle artifact from EEG data:Comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches [J]. Eurasip Journal on Advances in Signal Processing,2012,2012 (1):
[127]ZENG H, SONG A, YAN R, et al. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition [J]. Sensors (Switzerland),2013,13 (11):14839-14859.
[128]DYBAA J, ZIMROZ R. Empirical mode decomposition of vibration signal for detection of local disturbances in planetary gearbox used in heavy machinery system; proceedings of the 5th International Congress of Technical Diagnostics, September 3,2012-September 5,2012, Krakow, Poland, F,2014 [C]. Trans Tech Publications Ltd.
[129]MOLLAM K I, HIROSE K, MINEMATSU N. Separation of mixed audio signals by source localization and binary masking with Hilbert spectrum; proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006, March 5,2006-March 8,2006, Charleston, SC, United states, F,2006 [C]. Springer Verlag.
[130]MOLLA M K I, HIROSE K, MINEMATSU N. Localization based separation of mixed audio signals with binary masking of hilbert spectrum; proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, May 14,2006-May 19,2006, Toulouse, France, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[131]MOLLAR M K I, HIROSE K, MINEMATSU N. Localization based audio source separation by sub-band beamforming; proceedings of the ISC AS 2006:2006 IEEE International Symposium on Circuits and Systems, May 21,2006-May 24, 2006, Kos, Greece, F,2006 [C]. Institute of Electrical and Electronics Engineers Inc.
[132]CAI Y-P, LI A-H, WANG T, et al. I.C. engine vibration time-frequency analysis based on EMD-Wigner-Ville [J]. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering,2010,23 (4):430-437.
[133]TAKANO Y, YAMAMOTO Y. Metric-preservtng reduction of earth mover's distance [J]. Asia-Pacific Journal of Operational Research,2010,27 (1):39-54.
[134]WARBHE A D, DHARASKAR R V, KALAMBHE B. A single channel phonocardiograph processing using EMD, SVD, and EFICA; proceedings of the 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010, November 19,2010-November 21,2010, Goa, India, F,2010 [C]. IEEE Computer Society.
[135]YE H, YANG S, YANG J. Mechanical vibration source number estimation based on EMD-SVD-BIC [J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,2010,30 (3):330-334.
[136]JIANG Y, QIN L, ZHANG Y, et al. Vibration signal processing for gear fault diagnosis based on empirical mode decomposition and nonlinear blind source separation,5 Wates Way, Brentwood, Essex, CM15 9TB, United Kingdom, F, 2011 [C]. Multi-Science Publishing Co. Ltd.
[137]HAO Z, MA Z, ZHOU H. Research on fault diagnosis method based on empirical mode decomposition time-frequency reassignment; proceedings of the 2011 International Conference on Material Science and Information Technology, MSIT2011, September 16,2011-September 18,2011, Singapore, Singapore, F, 2012 [C]. Trans Tech Publications.
[138]LI Q, FU C, JIANG H, et al. Analysis of blind source separation for single channel vibration signal decomposition with a composite method [J]. International Journal of Digital Content Technology and its Applications,2012,6 (22):610-620.
[139]CAO W, FU P, XU G. Fault diagnosis of tool wear based on weak feature extraction and GA-B-spline network [J]. Sensors and Transducers,2013,152 (5): 60-67.
[140]WANG J, GAO R X, YAN R. Integration of EEMD and ICA for wind turbine gearbox diagnosis [J].2013,
[141]WANG Y, REN X, YANG Y, et al. An efficient denoising source separation (DSS) of rotating machine fault signals based on empirical mode decomposition (EMD) [J]. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University,2013,31 (2):272-276.
[142]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,(04)：12-16.
[143]BRONSTEIN M M, BRONSTEIN A M, ZIBULEVSKY M, et al. Reconstruction in diffraction ultrasound tomography using nonuniform FFT [J]. IEEE Transactions on Medical Imaging,2002,21 (11):1395-1401.
[144]BRONSTEIN A M, BRONSTEIN M M, ZIBULEVSKY M, et al. Separation of reflections via sparse ICA; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F,2003 [C]. SPIE.
[145]KISILEV P, ZIBULEVSKY M, ZEEVI Y Y. Application of wavelets in blind source separation; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F, 2003 [C]. SPIE.
[146]ZIBULEVSKY M. Blind Separation of Sparse Sources with Relative Newton Method; proceedings of the Wavelets:Applications in Signal and Image Processing X, August 4,2003-August 8,2003, San Diego, CA, United states, F, 2003 [C]. SPIE.
[147]左翠鹏.液压挖掘机驾驶室噪声控制研究[D].硕士学位论文：山东大学,2013.
[148]廖力达.挖掘机用柴油机噪声声源识别与特性研究[D].博士学位论文：中南大学,2012.
[149]窦青青.基于小波时频分析的工程机械驾驶室噪声源识别研究[D].硕士学位论文：山东大学,2012.
[150]郑旭.车辆与内燃机振声信号盲分离及噪声源识别的研究[D].博士学位论文：浙江大学,2012.
[151]HUANG N E, WU Z. ENSEMBLE EMPIRICAL MODE DECOMPOSITION:A NOISE-ASSISTED DATA ANALYSIS METHOD [J]. Advances in Adaptive Data Analysis,2009,01 (01):1-41.
[152]刘佳,杨士莪,朴胜春.基于eemd的地声信号单通道盲源分离算法[J].哈尔滨工程大学学报,2011,(02)：194-199.
[153]郑旭,郝志勇,金阳,等.采用改进的集总平均经验模态分解法的内燃机气门拍击激励与燃烧激励分离的研究[J].汽车工程,2011,(11)：930-936.
[154]周晓峰.机械振动源的分离和识别方法研究[D].博士学位论文：浙江大学,2012.
[155]信子君.通信信号与干扰信号的半盲分离算法研究[D].硕士学位论文：电子科技大学,2013.
[156]熊忻,杨世锡,周晓峰.旋转机械振动信号的固有模式函数降噪方法[J].浙江大学学报(工学版),2011,(08)：1376-1381.
[157]高峰.欠定松弛稀疏信号的盲分离研究[D].博士学位论文：华南理工大学,2012.
[158]叶红仙.机械系统振动源的盲分离方法研究[D].博士学位论文：浙江大学,2008.
[159]叶红仙,杨世锡,杨将新.基于EMD-SVD-BIC的机械振动源数估计方法[J].振动测试与诊断,2010,(03)：330-334+343.
[160]ANTONI J, GUILLET F, EL BADAOUI M, et al. Blind separation of convolved cyclostationary processes [J]. Signal Processing,2005,85 (1):51-66.
[161]张之猛.水声信号处理中的盲解卷积技术研究[D].博士学位论文：哈尔滨工程大学,2009.
[162]高鹰,谢胜利.基于信号稀疏特性和核函数的非线性盲信号分离算法[J].计算机工程与应用,2005,(01)：33-35.
[163]路亮,龙源,钟明寿,et al. FastICA算法在低信噪比爆破振动信号信噪分离中的应用研究[J].振动与冲击,2012,(17)：33-37.
[164]HYVARINEN A. Family of fixed-point algorithms for independent component analysis; proceedings of the Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP Part 1 (of 5), April 21,1997-April 24,1997, Munich, Ger, F,1997 [C]. IEEE.
[165]白树忠.欠定盲源分离算法及在语音处理中的应用研究[D].博士学位论文：山东大学,2008.
[166]HYVARINEN A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Transactions on Neural Networks,1999,10 (3): 626-634.
[167]李晶皎,安冬,王骄.基于eemd和ica的语音去噪方法[J].东北大学学报(自然科学版),2011,(11)：1554-1557.