一种听觉掩蔽效应和维纳滤波的语音增强算法

将非平稳噪声估计算法以及基于听觉掩蔽效应得到的噪声被掩蔽概率应用于维纳滤波语音增强中,提出了一种听觉掩蔽效应和维纳滤波的语音增强方法。几种噪声背景下对语音增强的客观测试表明,提出的算法相比较于传统的维纳滤波语音增强算法而言不但可以提高语音信噪比,而且可以明显减少语音失真。     

多通道助听器语音降噪算法研究

维纳滤波算法是改善噪声环境下听障患者语音理解度的常用算法之一。针对传统维纳滤波算法噪声谱估计偏差大的问题,提出一种基于改进的多通道维纳滤波算法的助听器语音降噪算法。算法首先结合人耳听觉特性和助听器响度补偿的特点,将语音信号进行Gammatone分解为多路子带信号。然后在每个子带内用基于先验信噪比估计的维纳滤波器进行语音增强处理。最后通过综合子带信号,得到增强的语音。此外,为了改善维纳滤波算法噪声谱估计的问题,提出一种基于包络估计的语音活动检测算法,并用于改善维纳滤波性能。实验结果表明,与传统维纳滤波法相比,该方法能更有效地抑制残留噪声,提高语音可懂度,具有较高的实用价值。     

子空间与维纳滤波相结合的语音增强方法

针对复杂背景噪声下语音增强后带有音乐噪声的问题,提出一种子空间与维纳滤波相结合的语音增强方法。对带噪语音进行KL变换,估计出纯净语音的特征值,再利用子空间域中的信噪比计算公式构成一个维纳滤波器,使该特征值通过这个滤波器,从而得到新的纯净语音特征值,由KL逆变换还原出纯净语音。仿真结果表明,在白噪声和火车噪声的背景下,信噪比都比传统子空间方法有明显提高,并有效抑制了增强后产生的音乐噪声。     

改进相位谱补偿的语音增强方法

针对传统单通道语音增强方法中用带噪语音相位代替纯净语音相位重建时域信号，使得语音主观感知质量改善受限的情况，提出了一种改进相位谱补偿的语音增强算法。该算法提出了基于每帧语音输入信噪比的Sigmoid型相位谱补偿函数，能够根据噪声的变化来灵活地对带噪语音的相位谱进行补偿；结合改进DD的先验信噪比估计与语音存在概率算法（SPP）来估计噪声功率谱；在维纳滤波中结合新的语音存在概率噪声功率谱估计与相位谱补偿来提高语音的增强效果。相比传统相位谱补偿（PSC）算法而言，改进算法可以有效抑制音频信号中的各类噪声，同时增强语音信号感知质量，提升语音的可懂度。     

联合听觉掩蔽效应的子空间语音增强算法

在经典子空间语音增强算法中,因语音特征值估计偏差会造成语音失真和音乐噪声。针对该问题,提出一种联合听觉掩蔽效应的语音增强算法。该算法联合掩蔽阈值自适应调节噪声特征值的抑制系数,并利用维纳滤波对音乐噪声的抑制性,对该特征值并行修正,最终还原出纯净的语音。实验结果证明,该算法在白噪声和有色噪声的背景下,与经典子空间的语音增强算法相比,能提高信噪比,减少语音失真和音乐噪声。     

基于小波包与自适应维纳滤波的语音增强算法

语音增强主要用来提高受噪声污染的语音可懂度和语音质量,它的主要应用与在嘈杂环境中提高移动通信质量有关。传统的语音增强方法有谱减法、维纳滤波、小波系数法等。针对复杂噪声环境下传统语音增强算法增强后的语音质量不佳且存在音乐噪声的问题,提出了一种结合小波包变换和自适应维纳滤波的语音增强算法。分析小波包多分辨率在信号频谱划分中的作用,通过小波包对含噪信号作多尺度分解,对不同尺度的小波包系数进行自适应维纳滤波,使用滤波后的小波包系数重构进而获取增强的语音信号。仿真实验结果表明,与传统增强算法相比,该算法在低信噪比的非平稳噪声环境下不仅可以更有效地提高含噪语音的信噪比,而且能较好地保存语音的谱特征,提高了含噪语音的质量。     

基于听觉掩蔽效应和最优平滑的语音增强算法

针对谱减法在低信噪比下音乐噪声较大的缺点,通过分析人耳听觉掩蔽特性,提出一种改进的语音增强算法。在维纳滤波法的基础上结合掩蔽效应调整增益系数,采用非平稳环境下的最小约束递归平均算法进行噪声参数估计,利用最小均方误差准则的最优平滑因子对增强语音进行平滑处理,从而进一步消除音乐噪声。仿真结果表明,与改进谱减法与维纳滤波法相比,该算法在低信噪比情况下能有效抑制背景噪声和残余的音乐噪声,保持较好的语音质量和清晰度。     

联合优化深度神经网络和约束维纳滤波的单通道语音增强方法

深度神经网络（Deep neural networks,DNNs）依靠其良好的特征提取能力,在语音增强任务中得到了广泛应用。为进一步提高深度神经网络的语音增强效果,提出一种将深度神经网络和约束维纳滤波联合训练优化的新型网络结构。该网络首先对带噪语音幅度谱进行训练并分别得到纯净语音和噪声的幅度谱估计,然后利用语音和噪声的幅度谱估计计算得到一个约束维纳增益函数,最后利用约束维纳增益函数从带噪语音幅度谱中估计出增强语音幅度谱作为网络的训练输出。对不同信噪比下的20种噪声进行的仿真实验表明,无论噪声类型是否在网络的训练集中出现,本文方法都能够在有效去除噪声的同时保持较小的语音失真,增强效果明显优于DNN及NMF增强方法。     

面罩语音质量评价算法适用性研究

针对语音编码的音质评价算法性能已十分明确,但对于面罩语音不一定适用。讨论了语音质量评价算法对空气语音与面罩语音在不同噪声环境下的适用性。采用主观意见得分和三种客观评价测度对多种信噪比的带噪语音和增强语音进行评价,包括分段信噪比、改进的巴克谱失真（MBSD）和语音感知质量评价（PESQ）,根据与主观评价的一致性判断客观评价方法的适用性。增强算法采用维纳滤波法和对数谱最小均方误差法（LSA-MMSE）,噪声采用粉红噪声、海浪噪声。仿真结果表明,语音质量评价算法的适用性与语音类型、信噪比、背景噪声、增强算法种类有关。粉红噪声环境下,PESQ不适合评价经维纳滤波增强的空气语音;MBSD算法只适用于评价经LSA-MMSE增强的面罩语音。海浪噪声环境下,PESQ适用于评价面罩语音,MBSD不适合评价面罩语音。     

一种新的强噪声环境下的语音增强算法

针对强噪声环境下语音增强中噪声估计和先验信噪比估计算法导致的语音失真和音乐噪声的问题,利用语音和噪声的统计模型的对称性得到一种噪声幅度的估计值为参考,提出了一种噪声估计算法,改进了先验信噪比估计算法,形成了一种新的增强算法,适用于强噪声环境下的语音增强。由仿真实验给出的客观评分看出,在0 dB乃至－5 dB条件下,给出信噪比估计算法能够有效减小信号失真,基本上没有残留音乐噪声。     

随机共振在强噪声环境中语音增强应用

传统的语音增强方法是在保持语音可懂度和清晰度的前提下,尽可能地从带噪语音中提取需要的纯净语音,而在强噪声环境中,语音信号表现为弱信号,去噪变得困难.基于Hodgkin-Huxley神经元阈上非周期随机共振原理,提出一种自适应调节,添加最佳噪声来进行语音随机共振,从而实现语音增强.Matlab实验结果表明,在强噪声环境中实现对语音信号增强,信噪比提高明显,且效果优于传统算法.方法具有一定鲁棒性,提供了在强噪声环境中增强语音信号的新思路.     

脉冲噪声环境下基于卡尔曼滤波的语音增强

语音增强的目标在于从含噪信号中提取纯净语音,纯净语音在某些环境下会被脉冲噪声所污染,但脉冲噪声的时域分布特征却给语音增强带来困难,使传统方法在脉冲噪声环境下难以取得满意效果。为在平稳脉冲噪声环境下进行语音增强,提出了一种新方法。该方法通过计算确定脉冲噪声样本的能量与含噪信号样本的能量之比最大的频段,利用该频段能量分布情况逐帧判别语音信号是否被脉冲噪声所污染。进一步地,该方法只在被脉冲噪声污染的帧应用卡尔曼滤波算法去噪,并改进了传统算法执行时的自回归(AR)模型参数估计过程。实验中,采用白色脉冲噪声以及有色脉冲噪声污染语音信号,并对低输入信噪比的信号进行语音增强,结果表明所提出的算法能显著地改善信噪比和抑制脉冲噪声。     

Speech enhancement using MMSE estimation under phase uncertainty

Most of the speech enhancement algorithms process the amplitudes of speech, but the phase of noisy speech is left unprocessed as it may cause undesired artifacts. Recently, short time Fourier transform based single channel speech enhancement algorithms are developed by considering uncertain prior knowledge of phase. The uncertain knowledge of the phase is obtained from the phase reconstruction algorithms. The goal of this paper is to develop joint minimum mean square error estimate of complex speech coefficients given uncertainty phase (CUP) information by considering Nagakami probability density function (PDF) and gamma PDF as speech spectral amplitude priors and generalized gamma PDF for noise prior. Estimators like amplitudes given uncertainty phase, which uses uncertain phase only for amplitude estimation and not for phase improvement are developed. Experimental results shows that incorporating uncertain phase information improves quality and intelligibility of speech. Also novel phase-blind estimators are developed using Nagakami PDF/gamma as speech priors and generalized gamma as noise prior. Finally comparison of all estimators using uncertain prior phase information is discussed and how initial phase information affects the enhancement process is analyzed with novel estimators. For comparison of all the derived estimators, the speech signals uttered by male and female speakers are taken from TIMIT database. The proposed CUP estimators outperforms the existing algorithms in terms of objective performance measure segmental signal to noise ratio, phase signal to noise ratio, perceptual evaluation of speech quality, short time objective intelligibility.     

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.     

Enhancement of noisy speech based on a custom thresholding function with a statistically determined threshold

Performance of the thresholding based speech enhancement methods largely depend on the estimate of the exact threshold value as well as on the choice of the thresholding function. In this paper, a speech enhancement method is presented, in which a custom thresholding function is proposed and employed upon the Wavelet Packet (WP) coefficients of the noisy speech. The thresholding function is capable of switching between modified hard and semisoft thresholding functions depending on a parameter that decides the signal characteristics under consideration. Here, the threshold is determined based on the statistical modeling of the Teager energy operated WP coefficients of the noisy speech. Extensive simulations indicate that the threshold thus obtained in conjunction with the custom thresholding function is very effective in reduction of not only the white noise but also the color noise from the noisy speech thus resulting in an enhanced speech with better quality and intelligibility. Several standard objective measures and subjective evaluations including informal listening tests show that the proposed method outperforms the recent state-of-the-art thresholding based approaches of noisy speech enhancement from high to low levels of SNR.     

Noise Tracking Using DFT Domain Subspace Decompositions

All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in speech quality, while an underestimation will lead to an unnecessary high level of residual noise. We present a novel approach for noise tracking, which updates the noise PSD for each DFT coefficient in the presence of both speech and noise. This method is based on the eigenvalue decomposition of correlation matrices that are constructed from time series of noisy DFT coefficients. The presented method is very well capable of tracking gradually changing noise types. In comparison to state-of-the-art noise tracking algorithms the proposed method reduces the estimation error between the estimated and the true noise PSD. In combination with an enhancement system the proposed method improves the segmental SNR with several decibels for gradually changing noise types. Listening experiments show that the proposed system is preferred over the state-of-the-art noise tracking algorithm.     

预加重与MMSE 结合的语音增强方法

基于语音周期性的特点,提出了一种预加重的MMSE语音增强的改进算法.首先,算法在尽量保留语音信息基础上对带噪语音预加重,使信噪比提高.预加重过程将根据带噪语音信号的周期性的强弱对信号动态加权.处理结果再作为MMSE语音增强的输入并将有利于提高最终的去噪效果.