基于阈值的小波域语音增强新算法

提出了一种新的基于阈值的小波域语音增强算法,采用Bark尺度小波包对含噪语音进行分解,以模拟人耳的听觉特性.采用结点阈值法,用基于谱熵的方法估计结点噪声,实验表明,该算法在多种噪声,尤其是有色噪声和非平稳噪声条件下均有较好的语音增强效果.     

基于非平稳噪声估计的改进谱减语音增强算法

传统的谱减法无法有效地抑制实际语音通信中的非平稳噪声,为了进一步提高谱减法的去噪性能,提出了一种改进的噪声估计算法,首先将带噪语音的功率谱按照Bark频率进行子带划分,然后分别在每个子带内利用改进的最小统计量控制递归平均方法跟踪噪声的变化,从而在准确估计非平稳噪声的功率谱的同时减少计算量。将该算法应用到谱减法中,并与传统的增强型谱减法进行对比实验,实验结果表明：改进的谱减法能够更好地去除各种非平稳噪声,而且能够有效抑制“音乐噪声”,使得增强后的语音具有更好的音质。     

Bark子带小波包自适应阈值语音去噪方法

为了克服低信噪比输入下,语音增强造成清音弱分量损失,导致信号重构失真的问题,提出了一种新的语音增强方法。该方法采用小波包拟合语音感知模型的临界带,按子带能量对语音清浊音分离,然后对清音和浊音信号分别作8层和4层小波包分解,在阈值计算上采用Bark子带小波包自适应节点阈值算法,在Bark子带实时跟踪噪声水平,有效保护清音中高频弱分量,减少失真。通过与传统语音增强方法的仿真对比实验,证实该方法在低信噪比输入时,具有明显优势,输出信噪比高,语音失真度低。将该方法与谱减法相结合,进行语音二次增强,能进一步提高增强语音质量。     

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。     

基于BARK子波变换的语音增强方法研究

在Bark子波的构造的基础上,提出一种改进的Bark子波变换构造方法,即直接由临界带中心频率确定Bark子波的中心频率,保证了其通带和临界带的对应一致性,并与人耳的听觉系统十分吻合。采用Bark子波对带噪语音进行分解,在语音信号的子带层次上用一种类似于软阈值的无穷阶可导的函数进行阈值处理,并应用谱减法进行二次增强。仿真实验表明,构建Bark子波与增强算法使信噪比和PESQ得分都有较大提高,特别是在信噪比较高时,语音具有很好的清晰度和可懂度。     

基于Bark小波的语音端点检测算法

为提高噪声环境下端点检测算法的性能,提出一种基于Bark小波变换的语音端点检测算法。在Bark小波变换的非线性映射中引入伸缩尺度参数,提取不同子带上信号的小波变换系数,利用Bark小波的奇异性构造端点检测参数,并通过仿真验证得到有效的阈值选择规则。实验结果表明,与传统的短时能量法和倒谱距离测度法相比,该算法的检测准确率明显提高,具有较强的噪声鲁棒性。     

基于感知滤波器与统计方法的语音增强新算法

根据人耳感知特性,提出了一种小波包感知滤波器与统计方法相结合的语音增强新算法.小波包感知滤波器根据人耳Bark域频率感知特性,将含噪语音频带划分成24个频率群,每个频率群内信号进行最小均方误差对数谱幅度(MMSE-LSA)的估计.通过估计各频率群的先验信噪比得到待估计语音与含噪语音的增益方程,从而得到该频率群内的估计语音,最后将所得的分段估计语音重建即得到增强后的语音.实验结果表明,在各种噪声情况下,该方法均优于其他方法.     

一种新阈值函数的小波包语音增强算法

针对传统软、硬阈值函数去噪方法增强的语音存在失真的问题,提出一种新阈值函数的小波包语音增强算法,同时给出了新阈值函数和新的Bark尺度小波包分解结构。新阈值函数在小波包系数绝对值大于给定阈值的区间内,灵活地结合了软、硬阈值函数;在小波包系数绝对值小于给定阈值的区间内,用一种非线性函数代替传统阈值函数中的简单置零,实现了阈值函数的平缓过渡;新的60个频带Bark尺度小波包分解结构能更好地模拟人耳的听觉感知特性。仿真实验结果表明,在高斯白噪声和有色噪声背景下,与传统软、硬阈值函数去噪方法相比,新算法有效提高了增强语音信噪比和分段信噪比,减少了语音失真,具有更好的去噪效果。     

一种改进的维纳滤波语音增强算法

提出了一种改进的语音增强算法,该算法以基于先验信噪比估计的维纳滤波法为基础。首先通过计算无声段的统计平均得到初始噪声功率谱;其次,计算语音段间带噪语音功率谱,并平滑处理初始噪声功率谱和带噪语音功率谱,更新了噪声功率谱;最后,考虑了某频率点处噪声急剧增大的情况,通过计算带噪语音功率谱与噪声功率谱的比值,自适应地调整噪声功率谱。将该算法与其他基于短时谱估计的语音增强算法进行了对比实验,实验结果表明：该算法能有效地减少残留噪声和语音畸变,提高语音可懂度。     

一种基于噪声对消与倒谱均值相减的鲁棒语音识别方法

提出一种基于语音增强算法的噪声鲁棒语音识别方法．在语音识别预处理阶段,通过噪声对消语音增强法来抑制噪声提高信噪比．然后对增强语音提取Mel频段倒谱特征参数,并在倒谱域应用倒谱均值相减处理来补偿增强语音中的失真成分和剩余噪声．实验结果表明,在低信噪比（-12—0 dB）条件下,该方法对于数字语音识别具有较好的识别率,其性能明显优于基本的Mel频段倒谱参数识别器、传统的谱减法和噪声对消语音增强法．     

Auditory driven subband speech enhancement for automatic recognition of noisy speech

Speech recognizers achieve high recognition accuracy under quiet acoustic environments, but their performance degrades drastically when they are deployed in real environments, where the speech is degraded by additive ambient noise. This paper advocates a two phase approach for robust speech recognition in such environment. Firstly, a front end subband speech enhancement with adaptive noise estimation (ANE) approach is used to filter the noisy speech. The whole noisy speech spectrum is portioned into eighteen dissimilar subbands based on Bark scale and noise power from each subband is estimated by the ANE approach, which does not require the speech pause detection. Secondly, the filtered speech spectrum is processed by the non parametric frequency domain algorithm based on human perception along with the back end building a robust classifier to recognize the utterance. A suite of experiments is conducted to evaluate the performance of the speech recognizer in a variety of real environments, with and without the use of a front end speech enhancement stage. Recognition accuracy is evaluated at the word level, and at a wide range of signal to noise ratios for real world noises. Experimental evaluations show that the proposed algorithm attains good recognition performance when signal to noise ratio is lower than 5 dB.     

基于改进深度置信网络的语音增强算法

研究了一种基于深度置信网络的语音增强算法，并针对其不足做如下改进：考虑到对应训练集中噪声种类较少，噪声特性不够丰富的情况，在频域对噪声频谱进行扰动，以丰富噪声频谱特性；考虑到不同频点的信号对系统误差的影响不一样，结合绝对听阈构造权重系数。最后选取在噪声环境下传统语音增强算法中较好的LOG-MMSE和本文改进的基于深度置信网络的语音增强算法进行了分析比较，结果证明深度置信网络的语音增强算法显示出较好性能，尤其对增强后语音质量的提升超过了LOG-MMSE方法。     

Multiple statistical models for soft decision in noisy speech enhancement

Most speech enhancement algorithms are based on the assumption that speech and noise are both Gaussian in the discrete cosine transform (DCT) domain. For further enhancement of noisy speech in the DCT domain, we consider multiple statistical distributions (i.e., Gaussian, Laplacian and Gamma) as a set of candidates to model the noise and speech. We first use the goodness-of-fit (GOF) test in order to measure how far the assumed model deviate from the actual distribution for each DCT component of noisy speech. Our evaluations illustrate that the best candidate is assigned to each frequency bin depending on the Signal-to-Noise-Ratio (SNR) and the Power Spectral Flatness Measure (PSFM). In particular, since the PSFM exhibits a strong relation with the best statistical fit we employ a simple recursive estimation of the PSFM in the model selection. The proposed speech enhancement algorithm employs a soft estimate of the speech absence probability (SAP) separately for each frequency bin according to the selected distribution. Both objective and subjective tests are performed for the evaluation of the proposed algorithms on a large speech database, for various SNR values and types of background noise. Our evaluations show that the proposed soft decision scheme based on multiple statistical modeling or the PSFM provides further speech quality enhancement compared with recent methods through a number of subjective and objective tests.     

基于噪声估计的二值掩蔽语音增强算法

针对现有的助听器语音增强算法在非平稳噪声环境下,残留大量背景噪声的同时还引入了“音乐噪声”,致使增强语音可懂度和信噪比不理想等问题。提出了一种基于噪声估计的二值掩蔽语音增强算法,该算法利用人耳听觉感知理论,结合人耳的听觉特性和耳蜗的工作机理。采用最小值控制递归平均（Minima-Controlled Recursive Averaging,MCRA）算法获得估计噪声和初步增强语音;将估计噪声和初步增强语音分别通过可以模拟人工耳蜗模型的gammatone滤波器组进行滤波处理,得到各自的时频表示形式;利用人耳的听觉掩蔽特性,计算含噪语音在时频域的二值掩蔽;利用二值掩蔽得到增强语音。实验结果表明：该算法很大程度上去除了谱减法引入的“音乐噪声”,与基于MCRA谱减法相比,增强语音的语言可懂度指数（Speech Intelligibility Index,SII）、主观语音质量评估（Perceptual Evaluation of Speech Quality,PESQ）和信噪比（Signal to Noise Ratio,SNR）都得到了提高。     

基于巴克谱的语音质量评估算法研究

提出了一种语音主观质量的客观评估算法，该算法在巴克谱的基础上计算原始语音与重建语音之间的失真度，并考虑了弱音帧与噪声帧的存在对语音质量评估的影响。文中同时给出了结合巴克谱失真和弱音与噪声帧比率的语音质量评估公式，并将计算结果与平均意见分(MOS)进行了比较。数值实验表明，本文提出的增强型巴克谱失真测度(IBSD)与MOS之间具有很强的相关性．能客观地评价出语音信号的主观质量，适用于各种语音编码、语音通信系统。     

基于改进能熵比的维纳滤波语音增强算法

为了提高低信噪比环境下语音增强的效果、算法的鲁棒性.在基于维纳滤波算法的基础上,结合基于频域特征的语音端点检查算法,提出了一种新的语音增强算法.端点检测算法使用小波包ERB子带的谱熵和改进的频域能量的能熵比法.其中,小波包ERB子带的谱熵考虑了人耳听觉掩蔽模型和语音与噪声信号之间的频率分布之间的不同;频域能量利用了有语音帧和无语音帧的能量不同.维纳滤波算法实时采集语音数据并使用新的参数来区别无语音段和有语音段,并在无语音段平滑更新噪声谱.实验结果表明,该端点检测算法能够很好的区分有语音段和无语音段,这就使得在低信噪比的情况下语音增强效果得到了提升,同时算法的鲁棒性和实时性也得到了保障.在与其他两种算法对比中,得到了更好的语音增强效果.     

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 based on undecimated wavelet packet-perceptual filterbanks and MMSE–STSA estimation in various noise environments

In this paper, we proposed a new speech enhancement system, which integrates a perceptual filterbank and minimum mean square error–short time spectral amplitude (MMSE–STSA) estimation, modified according to speech presence uncertainty. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree, according to critical bands of psycho-acoustic model of human auditory system. The MMSE–STSA estimation (modified according to speech presence uncertainty) was used for estimation of speech in undecimated wavelet packet domain. The perceptual filterbank provides a good auditory representation (sufficient frequency resolution), good perceptual quality of speech and low computational load. The MMSE–STSA estimator is based on a priori SNR estimation. A priori SNR estimation, which is a key parameter in MMSE–STSA estimator, was performed by using “decision directed method.” The “decision directed method” provides a trade off between noise reduction and signal distortion when correctly tuned. The experiments were conducted for various noise types. The results of proposed method were compared with those of other popular methods, Wiener estimation and MMSE–log spectral amplitude (MMSE–LSA) estimation in frequency domain. To test the performance of the proposed speech enhancement system, three objective quality measurement tests (SNR, segSNR and Itakura–Saito distance (ISd)) were conducted for various noise types and SNRs. Experimental results and objective quality measurement test results proved the performance of proposed speech enhancement system. The proposed speech enhancement system provided sufficient noise reduction and good intelligibility and perceptual quality, without causing considerable signal distortion and musical background noise.