面向二元麦克风小阵列改进的广义旁瓣抵消器语音增强算法

二元麦克风小阵列在手机、助听器等受空间、成本以及运算能力限制的设备中被广泛研究用以提高目标语音质量。二元麦克风小阵列中语音增强算法主要包括波束形成方法以及相干性滤波器方法。波束形成方法的思想是利用目标声源相对阵列的位置关系获取相应的时域和空域信息,可以保留目标声源方向的信号而抑制其他方向的干扰信号;相干性滤波器方法则通过阵元间不同信号的相关性进行噪音抑制。考虑这两种类型方法的优点,本文提出一种面向二元麦克风小阵列改进的广义旁瓣抵消器语音增强算法,通过在广义旁瓣抵消器的固定波束形成支路上使用相干性滤波器,提高固定波束形成输出信号的信噪比,然后在广义旁瓣抵消器自适应支路利用阵列的时域和空域信息对固定波束形成支路输出的信号中残余噪音进行估计,进而获得增强后目标输出信号。仿真和实际试验表明,本文提出的算法明显优于单独使用小阵列波束形成算法和相干性滤波器算法。      

麦克风阵列的协同自适应滤波语音增强方法

在麦克风阵列语音增强方法中,传统的广义旁瓣抵消器在处理存在显著脉冲噪声的语音信号时效果较差。为提高在脉冲噪声干扰下的语音信号增强效果,提出一种麦克风阵列的协同自适应滤波语音增强方法。该方法采用协同自适应滤波取代线性自适应滤波,基于NLMS算法导出了滤波器权值和协同因子的自适应更新算法。仿真实验结果表明,所提方法能有效地消除掉语音信号的脉冲噪声和高斯噪声,克服线性自适应滤波对非线性脉冲噪声的不敏感性,比广义旁瓣抵消器效果优越很多。     

基于盲波束形成的麦克风阵列语音增强方法

考虑到语音信号方向向量估计误差对传统波束形成语音增强性能的影响,该文提出一种盲波束形成语音增强方法。由于采用阵列四阶互累积量和线性约束最小方差波束形成器相结合,使得该方法对语音信号方向向量误差具有一定韧性。此外,采用多通道后置滤波去除盲波束形成器输出端的残留噪声。仿真结果表明,在语音信号波达方向等先验信息未知的情况下,该文提出的盲波束形成语音增强方法仍具有较好的噪声抑制性能。     

一种双微阵列语音增强方法

为提高语音通信系统在噪声环境下的使用性能,该文提出一种基于子带谱减与广义旁瓣抵消的双微阵列语音增强方法。基于双微阵列及子带结构分析,首先分别在低频带采用可变过减因子谱减法抑制噪声,在高频带采用修改互功率谱谱减法抑制非相干性噪声部分,再结合广义旁瓣抵消与端点检测进一步抑制强相关性噪声的影响。实验结果表明,该方法能够更加有效地抑制噪声的影响并提高语音的可懂度。     

基于子带TF-GSC麦克风阵列语音增强

为了加快基于传递函数广义旁瓣相消器的麦克风阵列语音增强系统的收敛速度,将其自适应模块的输入信号分解到子带以进行处理,并将多通道维纳滤波器引入传递函数广义旁瓣相消器的非自适应支路,以便更有效地抑制非相干噪声.实际测试结果表明,相对于基于全带广义旁瓣相消器的麦克风阵列语音增强系统,采用该子带传递函数广义旁瓣相消器结构的语音增强系统具有更高的输出信噪比.     

一种稳健的自适应波束形成器

当信号噪声比超过一定的门限时,线性约束自适应波束形成器对天线的幅相误差有很高的敏感度,即使在误差很小的情况下,期望信号也会如同干扰一样被抑制掉。该文通过对广义旁瓣相消器的阻塞矩阵加以改进,提出了一种对阵列天线误差有良好稳健性的自适应波束形成器。该方法基于广义旁瓣相消器结构,可方便地进行部分自适应,降低运算量。     

利用信号功率谱密度的麦克风阵列噪声消除方法

本文研究了一种在背景噪声和干扰噪声存在的情况下基于麦克风阵列的噪声消除方法,具有准确的指向性。波束形成可以更好的获取指定方向的增强语音及抑制其它方向的噪声的效果。而现已存在的波束形成的方法处理后,增强之后的语音仍然会存在部分的干扰噪声。针对这样的问题,本文提出了一种利用信号功率谱密度比值的广义旁瓣消除波束形成方法来进一步实现对背景噪声和干扰噪声的抑制。此外,本文还进一步利用深度神经网络的方法,通过训练多目标函数下的掩蔽值结合最优改进对数谱幅度,做后置滤波可以更高效地对残留干扰噪声进行消除。本文中通过对比实验,比较了不同的基线方法,更好地验证了所提出算法的有效性。      

一种基于子带GSC的语音增强算法

提出一种基于GSC的语音增强算法,该算法应用了DFT调制子带滤波器组将语音信号分解到子带进行自适应滤波,从而获得更好的增强效果以及更低的运量复杂度.同时,将范数约束自适应滤波(NCAF)算法应用于自适应噪声对消器(ANC)以降低语音的失真度.为了进一步去除增强后语音中的残留噪声,算法使用改进的Wiener后置滤波器.仿真结果表明,相对于基于全带GSC的麦克风阵列语音增强算法以及传统Wiener后置滤波算法,采用本文所用算法具有更高的输出分段信噪比.     

基于广义旁瓣相消器的滤波器组旁瓣干扰抑制

传统滤波器组为降低旁瓣,需要提高滤波器阶数。该文将广义旁瓣相消的思想运用于滤波器组的旁瓣干扰抑制,在滤波器通带外的干扰信号频率处自适应形成零点。基于LMS算法,该文提出了基于自适应旁瓣相消器的滤波器组旁瓣干扰抑制算法,给出了算法的矩阵形式。通过限制系数长度,旁瓣相消器仅仅对消旁瓣大功率干扰信号,而对带内有用信号的影响很小,其作用相当于用一个低阶滤波器实现一个高阶数滤波器的功能,当信号功率较之干扰功率很小时尤其有用。仿真结果显示算法具有良好的旁瓣干扰抑制性能,较之高阶滤波器组大大减少了计算量。     

利用相位时频掩蔽的麦克风阵列噪声消除方法

本文提出了一种在干扰声源和背景噪声存在条件下麦克风阵列噪声消除的方法。麦克风阵列通过波束形成增强由导向矢量所指定方向的目标声源来抑制背景噪声。然而,现有的波束形成算法在干扰声源存在的情况下,无法进行准确的导向矢量估计。为此,本文提出一种基于音频信号互相关功率谱相位的麦克风阵列噪声消除方法。首先通过音频信号的相位时频掩码估计导向矢量,并对其进行波束形成,从而有效抑制干扰声源和背景噪声;然后利用语音存在概率,采用最大似然的方法估计波束形成后信号中残留的干扰噪声功率谱密度,对其进行后处理,进一步抑制残留干扰和噪声。实验结果表明在干扰声源和背景噪声存在的条件下,所提方法有效地实现了麦克风阵列噪声消除,且各种性能指标优于基线方法。      

Phone-based filter parameter optimization of filter and sum robust speech recognition using likelihood maximization

Because of noise and reverberation, accuracy of speech recognition systems decreases when the distance between talker and microphone increases. By the using of microphone arrays and appropriate filtering of received signals, the accuracy of recognizer can be increased. Many different methods for using microphone arrays have been proposed that can be classified into two main approaches: systems that perform in two independent stages of array processing and then recognition and systems that use array processing to generate a sequence of features which maximize the likelihood of generating the correct hypothesis in recognition phase. Following second approach, in this paper a new method for microphone array processing is proposed in which the parameters of array processing are adjusted in calibration phase based on phones used in language and maximum likelihood method. Optimized filter parameters are stored and used during recognition phase. A new modified Viterbi algorithm using optimal phone-based filter parameters is used for recognition phase. The proposed algorithm is analytically formulated and Persian language is used to find any improvement in speech recognition accuracy compared with results of delay and sum and utterance-based filter and sum algorithms. The results show 12.2% improvement in accuracy compared to utterance-based algorithm.     

双传声器系统中的风噪声抑制方法研究

风噪声是自然界中最常见的一种噪声,严重影响着传声器拾音质量,并且其非平稳性使普通消噪方法（如谱减法等）不适用于风噪声抑制。本文分析了双传声器拾取的语声信号和风噪声信号的频域相干性,利用来自双传声器语声信号之间的强相干性和风噪声之间的弱相干性,采用Zelinski滤波器思路,考虑自由声场和扩散声场中风噪声和背景噪声的综合影响,设计了一种利用信号的相干性进行风噪声检测,进而准确估计风噪声相干系数的风噪声抑制滤波器。实验证明,文中提出的基于双传声器相干性原理的风噪声抑制方法较传统方法不仅在消噪性能上有较大提升,而且还具有运算量小、实时性强的特点,能够广泛应用于自由声场和扩散声场中的各类拾音系统。      

结合差分阵列与幅度谱减的双麦语音增强算法

为了抑制小型语音通信设备中的方向性噪声干扰问题,提出了一种结合差分阵列与幅度谱减的双麦语音增强算法。该算法首先利用一阶差分阵列技术,对两麦克风采集到的带噪语音信号进行处理,得到语音通道信号和噪声通道信号。接着利用差分阵列处理后的两通道信号对语音通道信号的信噪比进行估计。最后利用幅度谱减法对语音通道信号中残留噪声进行消除。针对语音通道信号的信噪比估计,本文给出了两种新奇的计算方法。仿真实验表明,该算法有效的抑制了方向噪声,改善了语音的质量,去噪效果及语音质量均优于对比算法。      

Two‐Microphone Generalized Sidelobe Canceller with Post‐Filter Based Speech Enhancement in Composite Noise

This paper describes an algorithm to suppress composite noise in a two‐microphone speech enhancement system for robust hands‐free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal‐dominant time‐frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech‐dominant TFBs are identified among the previously detected nonstationary signal‐dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin‐wise output signal‐to‐noise ratio is obtained with these power estimates and a Wiener post‐filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post‐filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.     

Source localization with distributed sensor arrays and partial spatial coherence

Multiple sensor arrays provide the means for highly accurate localization of the (x,y) position of a source. In some applications, such as microphone arrays receiving aeroacoustic signals from ground vehicles, random fluctuations in the air lead to frequency-selective coherence losses in the signals that arrive at widely separated sensors. We present performance analysis for localization of a wideband source using multiple, distributed sensor arrays. The wavefronts are modeled with perfect spatial coherence over individual arrays and frequency-selective coherence between distinct arrays, and the sensor signals are modeled as wideband, Gaussian random processes. Analysis of the Cramer-Rao bound (CRB) on source localization accuracy reveals that a distributed processing scheme involving bearing estimation at the individual arrays and time-delay estimation (TDE) between sensors on different arrays performs nearly as well as the optimum scheme while requiring less communication bandwidth with a central processing node. We develop Ziv-Zakai bounds for TDE with partially coherent signals in order to study the achievability of the CRB. This analysis shows that a threshold value of coherence is required in order to achieve accurate time-delay estimates, and the threshold coherence value depends on the source signal bandwidth, the additive noise level, and the observation time. Results are included based on processing measured aeroacoustic data from ground vehicles to illustrate the frequency-dependent signal coherence and the TDE performance.     

基于多任务疏表达的二元麦克风小阵列语音增强算法简

针对常规二元麦克风小阵列话音增强算法通常需要话音活动检测技术支持,并且难以有效抑制第一帧含目标信号的噪声。提出了一种基于多任务稀疏表达的二元麦克风小阵列话音增强算法,首先利用字典学习方法分别获得目标信号和噪声信号的过完备字典,然后利用 混合范数对信号在其字典上的表示系数进行正则化稀疏约束,使得2个阵元接收到信号中的噪声信号被抑制,而话音信号尽量保持不变,从而达到话音增强的目标。仿真和实验数据表明,无论开始位置是否含有目标话音信号,所提出的非话音活动检测支持的二元麦克风小阵列话音增强算法均能有效实现话音增强的目标。     

GSVD-based optimal filtering for single and multimicrophone speech enhancement

A generalized singular value decomposition (GSVD) based algorithm is proposed for enhancing multimicrophone speech signals degraded by additive colored noise. This GSVD-based multimicrophone algorithm can be considered to be an extension of the single-microphone signal subspace algorithms for enhancing noisy speech signals and amounts to a specific optimal filtering problem when the desired response signal cannot be observed. The optimal filter can be written as a function of the generalized singular vectors and singular values of a speech and noise data matrix. A number of symmetry properties are derived for the single-microphone and multimicrophone optimal filter, which are valid for the white noise case as well as for the colored noise case. In addition, the averaging step of some single-microphone signal subspace algorithms is examined, leading to the conclusion that this averaging operation is unnecessary and even suboptimal. For simple situations, where we consider localized sources and no multipath propagation, the GSVD-based optimal filtering technique exhibits the spatial directivity pattern of a beamformer. When comparing the noise reduction performance for realistic situations, simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.     

基于等边三角形的二阶差分传声器阵列

实现了一种等边三角形结构微型传声器阵列的语音增强方法.不同于以往的线性一阶差分传声器阵列结构和线性二阶差分传声器阵列结构,提出并且验证了一种基于延时相加的二阶三角差分传声器阵列的算法,通过真实环境的检测,证明该算法能够实现12个方向的语音增强,同时方向性信噪比比线性一阶差分传声器阵列增强3～4 dB.