改进增益函数的MMSE语音增强算法

讨论了基于语音短时对数谱最小均方误差(MMSE-STSA)的语音增强算法,将先验信噪比估计引入增益函数的计算中,有效消除噪声.在带噪信号模型中引入语音存在的不确定度,估计出每个频点的先验无声概率,对增益函数进行改进.通过客观与主观两种评价方法将改进算法与小波变换算法和MMSE估计算法进行比较,实验结果表明,改进算法能更好地抑制背景噪声并且使增强后的语音有较小的失真,增加语音清晰度和理解度.     

基于非因果先验信噪比估计的语音增强改进算法

为提高MMSE-LSA语音增强算法在低信噪比下的语音增强效果,提出一种改进的MMSE-LSA算法。该算法采用非因果先验信噪比估计法来估计先验信噪比,并引入无语音概率的思想,对增益函数进行改进。实验结果表明,相比传统MMSE-LSA算法,改进算法能更好地抑制残留噪声,提高语音的信噪比,增强效果更好。     

基于循环神经网络的实时语音增强算法

基于循环神经网络的RNNoise语音增强算法在非稳态噪声环境中有着优良的噪声抑制效果,但在应对未知噪声时,存在增益估计偏差、频带增益估计过平滑的问题,而基于统计模型的MMSE-LSA语音增强算法,在噪声估计不准确的情况下,也能取得良好的噪声抑制效果.为结合两者的优良特性,将RNNoise中的频带增益估计转换为频带先验信噪比作为神经网络的输入特征,结合基音检测算法修正谐波增益,提出一种可抗非稳态噪声的实时语音增强算法MMSE-RNNoise.对比实验验证了改进算法的可行性,其实时语音增强性能有了一定提升.     

基于增益字典查询的语音增强算法

对于基于统计模型的语音增强算法,不同分布模型对应于不同的增益函数,由于语音信号的不确定性,没有一种分布函数能准确对语音和噪声谱的分布建模,因此任何一种固定的统计模型均会存在一定的误差。所以提出一种增益字典查询的语音增强算法,该算法通过采用对数谱失真准则对一个语音噪声库进行增益的训练,得到一个增益的字典,其中输入为先验信噪比和后验信噪比的估计值。最后采用ITU-T P.826 PESQ、分段信噪比、总信噪比和对数谱失真对该算法进行了测试,并与基于高斯分布模型、拉普拉斯分布模型的算法进行了对比。实验结果表明,该算法无论在非平稳噪声还是平稳噪声环境下都比其他几种算法增强效果好,且音乐噪声和残留背景噪声也可以得到很好的抑制。     

运用MMSE先验信噪比估计进行语音增强

针对DD（Decision-Directed）先验信噪比估计方法在处理语音时产生延迟以及非因果先验信噪比估计算法不具实时性的缺点,提出一种MMSE（Minimum Mean Square Error）先验信噪比估计方法。它在高斯语音模型假设的基础上,运用最小均方误差准则直接从带噪信号中估计先验信噪比。通过对增强语音信噪比、Itakura-Saito失真测度以及信号时域图和语谱图仿真,结果表明,该算法比DD算法能更好地抑制“音乐噪声”和防止语音畸变,且相对于非因果先验信噪比估计算法具有更强实时性。     

基于听觉掩蔽效应的多频带谱减语音增强方法

为了减小传统谱减法引入的音乐噪声,提出了一种将多频带谱减和听觉掩蔽效应相结合的语音增强算法.用加权递归平滑的方法估计噪声的功率谱,对带噪的语音信号进行多频带谱减,计算听觉掩蔽阈值,再根据掩蔽阈值动态地调节谱减因子,通过增益函数得到增强后语音信号的频谱.仿真实验结果表明,与传统的谱减法相比,该算法在信噪比较低情况下,背景噪声和残余噪声得到了有效的抑制,语音信号的清晰度和可懂度也有了明显提升.     

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。     

改进相关统计模型的语音增强算法

语音和噪声的时频相关特性研究表明,"音乐噪声"区别于语音的一个重要特征是"音乐噪声"谱时频不相关.根据这一特点,在传统先验信噪比估计相关统计模型基础上给出了两点相关性补充假设.在此基础上,通过改进对数谱最小均方误差语音增强(LSA-MMSE)算法中的D-D先验信噪比估计,提出了改进对数谱最小均方误差语音增强算法.仿真实验采用了主观综合评分测度(MOS)和MBSD两种评价机制,实验结果表明,新模型和算法可以有效地抑制"音乐噪声"现象.     

两类噪声谱估计方法的对比分析

对基于传统端点检测技术和基于最小统计和平滑滤波的两类噪声估计方法进行了对比分析,并应用于谱相减算法中。针对基于最小统计和平滑滤波的谱减算法中出现的“音乐噪声”问题,提出了一种改进方案,实验表明,使用改进谱减法增强后的语音信号在信噪比和语音质量上均有提高。     

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。     

Simultaneous Detection and Estimation Approach for Speech Enhancement

In this paper, we present a simultaneous detection and estimation approach for speech enhancement. A detector for speech presence in the short-time Fourier transform domain is combined with an estimator, which jointly minimizes a cost function that takes into account both detection and estimation errors. Cost parameters control the tradeoff between speech distortion, caused by missed detection of speech components and residual musical noise resulting from false-detection. Furthermore, a modified decision-directed a priori signal-to-noise ratio (SNR) estimation is proposed for transient-noise environments. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach with the proposed a priori SNR estimator, which facilitate suppression of transient noise with a controlled level of speech distortion.     

A gain factor adapted by masking property and SNR variation for speech enhancement in colored-noise corruptions

A gain factor adapted by both the intra-frame masking properties of the human auditory system and the inter-frame SNR variation is proposed to enhance a speech signal corrupted by additive noise. In this article we employ an averaging factor, varying with time–frequency, to improve the estimate of the a priori SNR. In turn, this SNR estimate is utilized to adapt a gain factor for speech enhancement. This gain factor reduces the spectral variation over successive frames, so the effect of musical residual noise is mitigated. In addition, the simultaneous masking property of the human ears is also employed to adapt the gain factor. Imperceptive residual noise with energy below the noise masking threshold is retained, resulting in a reduction of speech distortion. Experimental results show that the proposed scheme can efficiently reduce the effect of musical residual noise.     

非因果先验信噪比估计的LSA算法改进

对于大多数的语音增强算法,先验信噪比及背景噪音频谱估计的准确与否,对语音增强的效果影响至关重要.为此,在传统MMSE-LSA算法的基础上,提出一种基于非因果先验信噪比估计的LSA 改进算法,较好地弥补了传统 LSA 算法在先验信噪比上估计的不足,同时采用平滑系数动态更新噪音频谱值,使估计值能更好地跟踪噪音的变化.实验结...     

Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise, but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore, the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called two-step noise reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the reverberation effect. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in enhanced speech because of the unreliability of estimators for small signal-to-noise ratios. This is mainly due to the difficult task of noise power spectrum density (PSD) estimation in single-microphone schemes. To overcome this problem, we propose a method called harmonic regeneration noise reduction (HRNR). A nonlinearity is used to regenerate the degraded harmonics of the distorted signal in an efficient way. The resulting artificial signal is produced in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics. These methods are analyzed and objective and formal subjective test results between HRNR and TSNR techniques are provided. A significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics.     

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.     

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。     

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。