基于信号子空间增强和端点检测的大地电磁噪声压制

为了进一步保留大地电磁低频段的有用信息、提高矿集区复杂噪声环境下大地电磁测深深部探测能力,在形态滤波的基础上结合信号子空间增强和端点检测做二次信噪分离处理.首先,针对形态滤波预提取的噪声轮廓运用信号子空间增强分离出信号子空间和噪声子空间.然后,将信号子空间和重构信号相结合并将噪声子空间置零.最后,借鉴端点检测做后处理,以识别波形突变的起止点.仿真结果表明,卡尼亚电阻率曲线在低频段的数据质量得到了明显改善、视电阻率值相对稳定;有效地补偿了形态滤波处理过程中损失的低频有用信号,其结果更加真实地反映了测点本身所固有的大地电磁深部构造信息.     

基于字典学习和稀疏表示的单通道语音增强算法综述*

如何从带噪语音信号中恢复出干净的语音信号一直都是信号处理领域的热点问题。近年来研究者相继提出了一些基于字典学习和稀疏表示的单通道语音增强算法,这些算法利用语音信号在时频域上的稀疏特性,通过学习训练数据样本的结构特征和规律来构造相应的字典,再对带噪语音信号进行投影以估计出干净语音信号。针对训练样本与测试数据不匹配的情况,有监督类的非负矩阵分解方法与基于统计模型的传统语音增强方法相结合,在增强阶段对语音字典和噪声字典进行更新,从而估计出干净语音信号。本文首先介绍了单通道情况下语音增强的信号模型,然后对4种典型的增强方法进行了阐述,最后对未来可能的研究热点进行了展望。     

Subspace algorithms for noise reduction in cochlear implants

A single-channel algorithm is proposed for noise reduction in cochlear implants. The proposed algorithm is based on subspace principles and projects the noisy speech vector onto "signal" and "noise" subspaces. An estimate of the clean signal is made by retaining only the components in the signal subspace. The performance of the subspace reduction algorithm is evaluated using 14 subjects wearing the Clarion device. Results indicated that the subspace algorithm produced significant improvements in sentence recognition scores compared to the subjects' daily strategy, at least in stationary noise. Further work is needed to extend the subspace algorithm to nonstationary noise environments.     

稀疏低秩噪声模型下无监督实时单通道语音增强算法

针对现有基于字典学习的增强算法需要先验信息、不易实时处理的问题,提出一种便于实时处理的无监督的单通道语音增强算法。首先,该算法将无监督条件下背景噪声的建模问题转化为带噪语音幅度谱的稀疏低秩噪声分解;然后,采用增量非负子空间方法对背景噪声进行在线字典学习,获得能够体现背景噪声时变特性的自适应噪声字典;最后,利用所得的噪声字典,采用易于实时处理的逐帧迭代方式,对带噪语音进行处理。实验结果表明:相较于多带谱减法和基于低秩稀疏矩阵分解的增强算法,所提算法在噪声抑制方面的性能尤为显著,在多项性能评价指标上,均表现出更好的结果。      

L1/2稀疏约束卷积非负矩阵分解的单通道语音增强方法

为了刻画语音信号帧间相关性和使用更少的语音基表示语音特征,提出一种采用L_1/2稀疏约束的卷积非负矩阵分解方法进行单通道语音增强。首先,进行噪声学习得到噪声基;然后,以噪声基为先验信息结合L_1/2稀疏约束卷积非负矩阵分解方法学习含噪语音中的语音基成分;最后,利用学习到的语音基和系数重建出干净语音信号。在不同噪声环境下进行的实验结果表明,本文方法优于采用L₁稀疏约束的卷积非负矩阵方法及传统的统计语音增强方法。      

Speech enhancement based on the discrete Gabor transform and multi-notch adaptive digital filters

This paper presents a new method to speech enhancement based on time-frequency analysis and adaptive digital filtering. The proposed method for dual-channel speech enhancement was developed by tracking frequencies of corrupting signal by the discrete Gabor transform (DGT) and implementing multi-notch adaptive digital filter (MNADF) at those frequencies. Since no a priori knowledge of the noise source statistics is required this method differs from traditional speech enhancement methods. Specifically, the proposed method was applied to the case where speech quality and intelligibility deteriorate in the presence of background noise. Speech coders and automatic speech recognition (ASR) systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The method uses a primary input containing the corrupted speech signal while a reference input containing the noise only. In this paper, we designed MNADF instead of single-notch adaptive digital filter and used DGT to track frequencies of corrupting signal because fast filtering process and fast measure of the time-dependent noise frequency are of great importance in speech enhancement process. Therefore, MNADF was implemented to take advantage of fast filtering process. Different types of noises from Noisex-92 database were used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR), Itakura-Saito distance measure as well as subjective listing test demonstrated consistently superior enhancement performance of the proposed method over traditional speech enhancement method such as spectral subtraction. Combining MNADF and DGT, excellent speech enhancement was obtained.     

Using power level difference for near field dual-microphone speech enhancement

In this contribution, a novel dual-channel speech enhancement technique is introduced. The proposed approach uses the dissimilarity between the power of received signals in the two channels as a criterion for speech enhancement and noise reduction. We claim that in near field conditions, where the distances between microphones and sound source are short, the difference in the received power levels at the two microphones is an estimate of the clean speech signal power. Then, apply this theory to present an optimum method for speech enhancement. Fortunately, the method has the ability to cope with problems such as transient noise and nearby microphones which are two of the main problems of the proposed dual-microphone speech enhancement techniques. Using objective speech quality measures and spectrogram analysis, we show that the proposed method results in improved speech quality.     

基于高斯-拉普拉斯-伽玛模型和人耳听觉掩蔽效应的信号子空间语音增强算法

针对信号子空间语音增强算法中的子空间选择和线性滤波器中噪声功率谱和拉格朗日乘子的估计问题,用高斯、拉普拉斯和伽玛模型描述了语音的分布,提出了利用目标语音概率最大化来确定信号子空间维度的方法。在噪声子空间上,利用条件概率估计出噪声功率谱。接着,为了合理地折中增强语音中的残余噪声和语音畸变,提出了一种基于人耳听觉掩蔽效应的拉格朗日乘子估计方法。实验证明,在多项语音质量评价指标上,所提算法都取得了更好的结果。所提的信号子空间算法比传统的信号子空间算法更有效地消除了噪声,使得恢复的语音具有更好的质量。      

语音通信降噪研究

语音通信系统中，语音通过信道传输将不可避免地引入码间串扰和信号畸变，同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上，考虑到自适应盲均衡技术在语音噪声控制方面能力有限，将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用，形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑，有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下，在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力，消噪同时可获得汉语普通话良好的听觉效果。     

A speech enhancement method based on Kalman filtering

I.IntroductionKa1manfilteringisjustamethodtoestimatestatistica1lythestateoftheobservedsystemfromthecorruptedsigna1s,andthiskindofcstimationisarecurrcneeestimationbasedon1inear,nonbiasandminimumvariance.Moreover,Ka1manfilteringisapplicabletonon-sta-honarysignalsandtime-variantdynamicsystem.Therefore,Kalmanfilteringisveryapplica-bletoenhancingthespeechsigna1sthatarecorruptedbynoise.ThispaperreportStheconcretcmethodofenhanccmentofnoisyspccchanditscxperimentresults.Experimentsindicate:Afterthes…     

Noise reduction using three-step gain factor and iterative-directional-median filter

Musical residual noise is a major problem for a speech enhancement system. This noise is very annoying to the human ear and can significantly deteriorate the perception quality of enhanced speech. In this study, we aim at reducing the quantity of musical residual noise by a two-stage speech enhancement approach. In the first stage a preprocessor enhances noisy speech using an algorithm which combines the two-step-decision-directed and the Virag methods. In the second stage the enhanced speech signal is post-processed by an iterative-directional-median filter to significantly reduce the quantity of residual noise, while maintaining the harmonic spectra. Experimental results show that the proposed approach can significantly improve the performance of a speech enhancement system by reducing the quantity of residual noise.     

Audio-visual enhancement of speech in noise.

A key problem for telecommunication or human-machine communication systems concerns speech enhancement in noise. In this domain, a certain number of techniques exist, all of them based on an acoustic-only approach--that is, the processing of the audio corrupted signal using audio information (from the corrupted signal only or additive audio information). In this paper, an audio-visual approach to the problem is considered, since it has been demonstrated in several studies that viewing the speaker's face improves message intelligibility, especially in noisy environments. A speech enhancement prototype system that takes advantage of visual inputs is developed. A filtering process approach is proposed that uses enhancement filters estimated with the help of lip shape information. The estimation process is based on linear regression or simple neural networks using a training corpus. A set of experiments assessed by Gaussian classification and perceptual tests demonstrates that it is indeed possible to enhance simple stimuli (vowel-plosive-vowel sequences) embedded in white Gaussian noise.     

结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。      

听觉频域掩蔽效应的自适应β阶贝叶斯感知估计语音增强算法

为了在噪声抑制和语音失真中之间寻找最佳平衡,提出了一种听觉频域掩蔽效应的自适应β阶贝叶斯感知估计语音增强算法,以期提高语音增强的综合性能。算法利用了人耳的听觉掩蔽效应,根据计算得到的频域掩蔽阈自适应调整β阶贝叶斯感知估计语音增强算法中的β值,从而仅将噪声抑制在掩蔽阈之下,保留较多的语音信息,降低语音失真。并分别用客观和主观评价方式,对所提出的算法的性能进行了评估,并与原来基于信噪比的自适应β阶贝叶斯感知估计语音增强算法进行了比较。结果表明,频域掩蔽的β阶贝叶斯感知估计方法的综合客观评价结果在信噪比为-10 dB至5 dB之间时均高于基于信噪比的自适应β阶贝叶斯感知估计语音增强算法。主观评价结果也表明频域掩蔽的β阶贝叶斯感知估计方法能在尽量保留语音信息的同时,较好的抑制背景噪声。      

基于修正Mel域掩蔽模型和无语音概率的耳语音增强

提出了一种基于修正Mel域听觉掩蔽模型和无语音概率的耳语音增强方法。该方法根据耳语音的发音特点对Mel频率进行修正,对每一帧耳语音信号进行Mel域频带滤波,同时通过无语音概率(SAP)动态地确定每个频带的听觉掩蔽阈值,对不同的听觉掩蔽阈值自适应地调整谱减系数来进行耳语音增强。对增强后的耳语音进行客观和主观测试,结果表明,该方法与其它谱减法相比,能将残留噪声和背景噪声控制在人耳掩蔽阈值下,取得更小的语音失真,主观听觉也得到了很大的改善。      

Model-based speech enhancement using a bone-conducted signal

Codebook-based single-microphone noise suppressors, which exploit prior knowledge about speech and noise statistics, provide better performance in nonstationary noise. However, as the enhancement involves a joint optimization over speech and noise codebooks, this results in high computational complexity. A codebook-based method is proposed that uses a reference signal observed by a bone-conduction microphone, and a mapping between air- and bone-conduction codebook entries generated during an offline training phase. A smaller subset of air-conducted speech codebook entries that accurately models the clean speech signal is selected using this reference signal. Experiments support the expected improvement in performance at low computational complexity.     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

A subspace approach based on embedded prewhitening for voice activity detection

This paper presents a subspace approach for voice activity detection (VAD). The proposed approach is based on an embedded prewhitening scheme for the simultaneous diagonalization of the clean speech and noise covariance matrices to provide a decision rule based on likelihood ratio test in signal subspace domain. Experimental results show that the proposed subspace-based VAD algorithm outperforms the method using a Gaussian model in a conventional discrete Fourier transform domain at the low signal-to-noise conditions.     

Speech enhancement method based on low-rank approximation in a reproducing kernel Hilbert space

Speech signal is corrupted unavoidably by noisy environment in subway, factory, and restaurant or speech from other speakers in speech communication. Speech enhancement methods have been widely studied to minimize noise influence in different linear transform domain, such as discrete Fourier transform domain, Karhunen-Loeve transform domain or discrete cosine transform domain. Kernel method as a nonlinear transform has received a lot of interest recently and is commonly used in many applications including audio signal processing. However this kind of method typically suffers from the computational complexity. In this paper, we propose a speech enhancement algorithm using low-rank approximation in a reproducing kernel Hilbert space to reduce storage space and running time with very little performance loss in the enhanced speech. We also analyze the root mean squared error bound between the enhanced vectors obtained by the approximation kernel matrix and the full kernel matrix. Simulations show that the proposed method can improve the computation speed of the algorithm with the approximate performance compared with that of the full kernel matrix.     

Patch-wise denoising of phase fringe patterns based on matrix enhancement

We propose a new approach for the denoising of a phase fringe pattern recorded in an optical interferometric setup. The phase fringe pattern which is generally corrupted by high amount of speckle noise is first converted into an exponential phase field. This phase field is divided into a number of overlapping patches. Owing to the small size of each patch, the presence of a simple structure of the interference phase is assumed in it. Accordingly, the singular value decomposition (SVD) of the patch allows us to separate the signal and noise components effectively. The patch is reconstructed only with the signal component. In order to further improve the robustness of the proposed method, an enhanced data matrix is generated using the patch and the SVD of this enhanced matrix is computed. The matrix enhancement results in an increased dimension of the noise subspace which thus accommodates more amount of noise component. Reassignment of the filtered pixels of the preceding patch in the current patch improves the noise filtering accuracy. The fringe denoising capability in function of the noise level and the patch size is studied. Simulation and experimental results are provided to demonstrate the practical applicability of the proposed method.