基于HHT复杂环境下低信噪比语音检测及增强方法研究
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摘要
随着信息处理技术手段的发展,语音信号应用领域的不断扩展,对语音信号处理的要求也越来越高。如语音识别技术,要求存在背景噪声的环境下达到有效识别；语音通信技术,要求在不影响接收语音信号质量的前提下,尽可能降低无语音段的数据传输；军事上,有效截获对方的信息和高效传输信息是电子对抗的重要组成部分。然而在复杂声学环境下,由于强背景噪声的影响,使得上述情况的语音信号变得相对较微弱,如室外嘈杂环境的语音通信与识别,短波通信噪声较强情况下的信息截获等,然而,这些情况往往都是经常出现的。为此,在复杂背景情况下,能否有效地对低信噪比语音信号进行检测与增强具有重要意义。
时频分析是语音信号检测与增强的有效手段之一。传统的时频分析算法主要有短时傅里叶、小波变换等,这些算法需要根据经验人为的设定信号分解尺度,而这些尺度有时并不能完全反应出信号的特征。此外,像小波变换需要根据变换对象事先选定小波基,而固定的小波基并不一定适合整个信号的分析。据此,本文选用一种可以根据信号特征自适应选取基函数及滤波尺度的算法——希尔伯特-黄变换。该算法特别适合分析像语音信号这样的非平稳信号。
本文针对希尔伯特-黄变换在低信噪比语音信号检测及增强方面展开研究,主要研究成果如下：
首先,采用了一种基于希尔伯特-黄变换的低信噪比语音信号检测算法,解决高斯噪声环境下低信噪比语音信号的检测问题。该算法的核心思想是,利用经验模态分解的自适应分解特性,将低信噪比语音信号中噪声含量较多的固有模态分量筛分出来,通过其它固有模态分量构建希尔伯特能量谱,从而实现低信噪比语音信号的检测；
其次,提出了一种基于希尔伯特-黄变换谱矩阵的低信噪比语音信号检测算法,解决多噪声环境下低信噪比语音信号的检测问题。该算法的核心思想是,利用语音信号与噪声信号的希尔伯特-黄变换谱特征的不同,实现低信噪比语音信号的检测。在构建低信噪比语音信号的希尔伯特-黄变换谱矩阵的过程中,考虑到能量集中问题,所以采用以帧为单位,构建该信号的谱矩阵。为了更有效、直观地分析谱矩阵,提出一种三维可视化分析方法。有效地找到了语音与噪声在谱矩阵中的区别,通过该区别设定滤波参数,对矩阵实施权值滤波,滤波后将谱矩阵转换成以帧为单位的二维时-幅曲线,自适应计算阈值,实现语音段的检测；
再次,采用极值域均值模式分解算法,对经验模态分解的曲线拟合、端点效应进行了改进。该算法的核心思想是,利用信号的所有信息,构建均值曲线。在构造信号的均值曲线时,通过极值点及极值点间的所有数据,利用积分中值定理的原理,寻找一个信号中的实际存在的值作为均值,使求取的均值更能反映输入信号的真实均值,提高了对局域均值的估计精度,降低了端点效应；
再次,提出一种基于极值域均值模式分解最大相似度的低信噪比语音增强算法,解决部分噪声环境下低信噪比语音信号增强问题。该算法核心思想是,对分解后得到的固有模态分量进行筛选后再做滤波处理,以此减少过滤波和欠滤波情况的发生。在筛选过程中,提出一种最大相似度判断算法,通过检测得到的噪声信号与固有模态分量计算最大相似度,通过最大相似度筛选出固有模态分量进行滤波,由于噪声与语音信号容易发生频谱混叠,在滤波器的选择上采用时域滤波器。将滤波后的固有模态分量和未作处理的固有模态分量进行信号重构,得到增强后结果；
最后,提出了一种基于极值域均值模式分解与独立分量分析相结合的低信噪比语音增强算法,解决更多噪声环境下低信噪比语音信号增强问题。该算法的核心思想是,利用独立分量分析的特点,分离出选取的固有模态分量的固有特性,消除信息混淆。通过最大相似度,筛选出需要处理的固有模态分量,对其进行独立分量分析,使噪声特性能够进一步集中,提高最大相似度,这样更有利于噪声的滤除。由于独立分量分析存在幅值、位置的不确定性,所以对滤波后的独立分量要进行二度重构,即独立分量分析重构和极值域均值模式分解重构,得到增强后结果。
本文从复杂环境下低信噪比语音信号的希尔伯特-黄变换的特性出发,围绕着低信噪比语音信号的检测与增强,针对不同噪声环境进行了研究,提出了相应的算法,并对所提出的算法进行了性能分析和对比实验,最终解决了复杂环境下低信噪比语音信号的检测与增强。
With the development of information processing technical means and the extension of speech signals application field, speech signals processing have been increasingly required. Speech recognition technology requires to achieve effective recognition in the background noise. Speech communication technology requires to decrease data transmission in the no speech band as much as possible without affecting speech signals quality received. Intercepting opponent information and transmiting information effectively are the important component in the military. However, in the complicated acoustics environment, the speech signals of above situation became weaker relatively due to the effect of strong background noise, such as speech communication and recognition in the noisy environment outside, information interception in the HF communication with the stronger noise etc.The situations have usually appeared. Therefore, it has significant meaning to detect and enhance speech signal with low SNR effectively in the complicated background environment.
Time-frequency analysis is one of the effective means in the speech signals detection and enhancement.Traditional time-frequency analysis algorithm have short-time fourier and wavelet transform etc. The algorithms need to set signal decomposition scale artificially by experience.However, the scales doesn't sometimes response signal characteristics. Furthermore, wavelet transform need to choose wavelet base beforehand by transform object, whereas fixed wavelet base has not fit the whole signal analysis. According to this, the article selected basic function and filter scale algorithm adaptively by signal characteristics, Hilbert-Huang Transform. The algorithm is specially proper to analyze non-stationary signals.
We researched speech signal detection and enhancement with low SNR based on HHT. The main achievements includes these contents.
Firstly, the paper adopted a speech signal detection algorithm with low SNR based on HHT for solving the speech signals detection with low SNR in Gauss environment. The core of the algorithm is to separate IMFs of more noise in the speech signals with low SNR by adaptive decomposition characteristic of EMD. According to other IMFs constructing Hilbert energy spectrum, speech signals with low SNR have been detected.
Secondly, the paper presented a detection algorithm of speech signals with low SNR based on HHTSM for solving the speech signals detection in many environments. The core of the algorithm is to realize speech signals detection by the difference of HHT spectrum between speech signals and noise signals. Constructing HHTSM of speech signals with low SNR, energy concentration has been considered, so the paper adopted a frame as unit and constructed spectrum matrix of the signal. The article presented a 3D viewable analysis method for analyzing spectrum matrix effectively. It effectively found the difference between speech and noise in the spectrum matrix, and implemented weight filter to the matrix by the difference setting filter coefficient. Spectrum matrix has been transformed 2D time-amplitude curve frame as unit after filter, and it calculated threshold adaptively. Speech band has been detected.
Then, the paper adopted EMMD algorithm to improve curve fitting and endpoint effect of EMD. The core of the algorithm is to construct mean curve by the whole information of signals. Constructing mean curve of signals, integral mean value theorem has been applied by all datas among the extreme points. Practical value has been found in the a signal as means. Obtained means responsed the true means of input signals. It improved estimation accuracy of local mean, and reduced endpoint effect.
Additionally, the paper presented a speech signal with low SNR algorithm based on EMMD for solving the speech signals enhancement with low SNR in any environments. The core of the algorithm is to filter after screening decomposited IMFs, preventing over-filter and owe-filter. The article presented a maximum similarity judgement algorithm by the maximum similarity of noise signals and IMFs. As noise and speech signals easily spectrum aliasing, we adopted time domain filter by the maximum similarity screening IMFs. The new signal has been reconstructed by IMFs which has been filter and IMFs which has been no processing. Then enhancement results have been obtained.
Eventually, it presented a speech signal enhancement algorithm with low SNR based on EMMD and ICA for solving the speech signals enhancement with low SNR in more environments. The core of the algorithm is to separate intrinsic properties of IMFs and to eliminate information confusion by ICA. To improve maximum similarity, IMFs which have been screened are processed by ICA. It was beneficial to concentrate noise properties and eliminate noise. ICA after filter must be twice reconstruction, which are ICA reconstruction and EMMD reconstruction, as the unconcerned amplitude and location of ICA. The enhancement results have been obtained.
The dissertation presented relevant algorithms aimed to different noise environments, with detection and enhancement of speech signals with low SNR by HHT characteristics of speech signals with low SNR in the complicated environment. It analyzed properties and experiments of the algorithms. Finally, the speech signal detection and enhancement with low SNR have been solved.
时频分析是语音信号检测与增强的有效手段之一。传统的时频分析算法主要有短时傅里叶、小波变换等,这些算法需要根据经验人为的设定信号分解尺度,而这些尺度有时并不能完全反应出信号的特征。此外,像小波变换需要根据变换对象事先选定小波基,而固定的小波基并不一定适合整个信号的分析。据此,本文选用一种可以根据信号特征自适应选取基函数及滤波尺度的算法——希尔伯特-黄变换。该算法特别适合分析像语音信号这样的非平稳信号。
本文针对希尔伯特-黄变换在低信噪比语音信号检测及增强方面展开研究,主要研究成果如下：
首先,采用了一种基于希尔伯特-黄变换的低信噪比语音信号检测算法,解决高斯噪声环境下低信噪比语音信号的检测问题。该算法的核心思想是,利用经验模态分解的自适应分解特性,将低信噪比语音信号中噪声含量较多的固有模态分量筛分出来,通过其它固有模态分量构建希尔伯特能量谱,从而实现低信噪比语音信号的检测；
其次,提出了一种基于希尔伯特-黄变换谱矩阵的低信噪比语音信号检测算法,解决多噪声环境下低信噪比语音信号的检测问题。该算法的核心思想是,利用语音信号与噪声信号的希尔伯特-黄变换谱特征的不同,实现低信噪比语音信号的检测。在构建低信噪比语音信号的希尔伯特-黄变换谱矩阵的过程中,考虑到能量集中问题,所以采用以帧为单位,构建该信号的谱矩阵。为了更有效、直观地分析谱矩阵,提出一种三维可视化分析方法。有效地找到了语音与噪声在谱矩阵中的区别,通过该区别设定滤波参数,对矩阵实施权值滤波,滤波后将谱矩阵转换成以帧为单位的二维时-幅曲线,自适应计算阈值,实现语音段的检测；
再次,采用极值域均值模式分解算法,对经验模态分解的曲线拟合、端点效应进行了改进。该算法的核心思想是,利用信号的所有信息,构建均值曲线。在构造信号的均值曲线时,通过极值点及极值点间的所有数据,利用积分中值定理的原理,寻找一个信号中的实际存在的值作为均值,使求取的均值更能反映输入信号的真实均值,提高了对局域均值的估计精度,降低了端点效应；
再次,提出一种基于极值域均值模式分解最大相似度的低信噪比语音增强算法,解决部分噪声环境下低信噪比语音信号增强问题。该算法核心思想是,对分解后得到的固有模态分量进行筛选后再做滤波处理,以此减少过滤波和欠滤波情况的发生。在筛选过程中,提出一种最大相似度判断算法,通过检测得到的噪声信号与固有模态分量计算最大相似度,通过最大相似度筛选出固有模态分量进行滤波,由于噪声与语音信号容易发生频谱混叠,在滤波器的选择上采用时域滤波器。将滤波后的固有模态分量和未作处理的固有模态分量进行信号重构,得到增强后结果；
最后,提出了一种基于极值域均值模式分解与独立分量分析相结合的低信噪比语音增强算法,解决更多噪声环境下低信噪比语音信号增强问题。该算法的核心思想是,利用独立分量分析的特点,分离出选取的固有模态分量的固有特性,消除信息混淆。通过最大相似度,筛选出需要处理的固有模态分量,对其进行独立分量分析,使噪声特性能够进一步集中,提高最大相似度,这样更有利于噪声的滤除。由于独立分量分析存在幅值、位置的不确定性,所以对滤波后的独立分量要进行二度重构,即独立分量分析重构和极值域均值模式分解重构,得到增强后结果。
本文从复杂环境下低信噪比语音信号的希尔伯特-黄变换的特性出发,围绕着低信噪比语音信号的检测与增强,针对不同噪声环境进行了研究,提出了相应的算法,并对所提出的算法进行了性能分析和对比实验,最终解决了复杂环境下低信噪比语音信号的检测与增强。
With the development of information processing technical means and the extension of speech signals application field, speech signals processing have been increasingly required. Speech recognition technology requires to achieve effective recognition in the background noise. Speech communication technology requires to decrease data transmission in the no speech band as much as possible without affecting speech signals quality received. Intercepting opponent information and transmiting information effectively are the important component in the military. However, in the complicated acoustics environment, the speech signals of above situation became weaker relatively due to the effect of strong background noise, such as speech communication and recognition in the noisy environment outside, information interception in the HF communication with the stronger noise etc.The situations have usually appeared. Therefore, it has significant meaning to detect and enhance speech signal with low SNR effectively in the complicated background environment.
Time-frequency analysis is one of the effective means in the speech signals detection and enhancement.Traditional time-frequency analysis algorithm have short-time fourier and wavelet transform etc. The algorithms need to set signal decomposition scale artificially by experience.However, the scales doesn't sometimes response signal characteristics. Furthermore, wavelet transform need to choose wavelet base beforehand by transform object, whereas fixed wavelet base has not fit the whole signal analysis. According to this, the article selected basic function and filter scale algorithm adaptively by signal characteristics, Hilbert-Huang Transform. The algorithm is specially proper to analyze non-stationary signals.
We researched speech signal detection and enhancement with low SNR based on HHT. The main achievements includes these contents.
Firstly, the paper adopted a speech signal detection algorithm with low SNR based on HHT for solving the speech signals detection with low SNR in Gauss environment. The core of the algorithm is to separate IMFs of more noise in the speech signals with low SNR by adaptive decomposition characteristic of EMD. According to other IMFs constructing Hilbert energy spectrum, speech signals with low SNR have been detected.
Secondly, the paper presented a detection algorithm of speech signals with low SNR based on HHTSM for solving the speech signals detection in many environments. The core of the algorithm is to realize speech signals detection by the difference of HHT spectrum between speech signals and noise signals. Constructing HHTSM of speech signals with low SNR, energy concentration has been considered, so the paper adopted a frame as unit and constructed spectrum matrix of the signal. The article presented a 3D viewable analysis method for analyzing spectrum matrix effectively. It effectively found the difference between speech and noise in the spectrum matrix, and implemented weight filter to the matrix by the difference setting filter coefficient. Spectrum matrix has been transformed 2D time-amplitude curve frame as unit after filter, and it calculated threshold adaptively. Speech band has been detected.
Then, the paper adopted EMMD algorithm to improve curve fitting and endpoint effect of EMD. The core of the algorithm is to construct mean curve by the whole information of signals. Constructing mean curve of signals, integral mean value theorem has been applied by all datas among the extreme points. Practical value has been found in the a signal as means. Obtained means responsed the true means of input signals. It improved estimation accuracy of local mean, and reduced endpoint effect.
Additionally, the paper presented a speech signal with low SNR algorithm based on EMMD for solving the speech signals enhancement with low SNR in any environments. The core of the algorithm is to filter after screening decomposited IMFs, preventing over-filter and owe-filter. The article presented a maximum similarity judgement algorithm by the maximum similarity of noise signals and IMFs. As noise and speech signals easily spectrum aliasing, we adopted time domain filter by the maximum similarity screening IMFs. The new signal has been reconstructed by IMFs which has been filter and IMFs which has been no processing. Then enhancement results have been obtained.
Eventually, it presented a speech signal enhancement algorithm with low SNR based on EMMD and ICA for solving the speech signals enhancement with low SNR in more environments. The core of the algorithm is to separate intrinsic properties of IMFs and to eliminate information confusion by ICA. To improve maximum similarity, IMFs which have been screened are processed by ICA. It was beneficial to concentrate noise properties and eliminate noise. ICA after filter must be twice reconstruction, which are ICA reconstruction and EMMD reconstruction, as the unconcerned amplitude and location of ICA. The enhancement results have been obtained.
The dissertation presented relevant algorithms aimed to different noise environments, with detection and enhancement of speech signals with low SNR by HHT characteristics of speech signals with low SNR in the complicated environment. It analyzed properties and experiments of the algorithms. Finally, the speech signal detection and enhancement with low SNR have been solved.
引文
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