Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain

This paper proposes a speech enhancement approach, which statistically determines an adaptive threshold using the Teager energy operated WP coefficients of noisy speech. The obtained threshold is employed upon the WP coefficients of the noisy speech by employing a modified hard thresholding function. Extensive simulations in the presence of different noises indicate that this new method is very effective for both white noise and color noise reduction from speech, resulting in enhanced speech with better speech quality. Several standard objective measures and subjective observations show that the proposed method outperforms recent state-of-the-art thresholding based approaches from high to low level SNRs.     

基于新阈值函数和自适应阈值的小波包语音增强研究

针对传统的小波包语音增强算法增强后的语音失真严重的问题,本文提出了一种基于自适应阈值和新阈值函数的小波包语音增强算法。该算法在小波包域将带噪语音加窗分帧,基于相邻帧快速傅立叶变换功率谱的互相关值,计算各帧存在语音的概率,然后通过语音存在概率对传统通用小波包阈值进行调整,使得阈值在非语音帧中较大,在语音帧中较小,实现阈值的自适应调整,可以在最大程度消除噪声的同时,尽可能的保留语音,减小语音失真。本文还设计了一种新阈值函数,克服了传统硬阈值函数不连续和软阈值函数会带来恒定偏差的缺点,进一步减小了语音失真。本文采用TIMIT 数据库和NOISEX-92 数据库中的语音和噪声进行了大量的模拟实验,主观评比和客观评比结果均证明本文提出的语音增强算法比现有的两种算法有更好的增强效果,采用本文算法增强后的语音失真更小,听觉效果更好。     

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.     

A wavelet- based transform method for quality improvement in noisy speech patterns of Arabic language

This paper addresses the problem of single-channel speech enhancement of low (negative) SNR of Arabic noisy speech signals. For this aim, a binary mask thresholding function based coiflet5 mother wavelet transform is proposed for Arabic speech enhancement. The effectiveness of binary mask thresholding function based coiflet5 mother wavelet transform is compared with Wiener method, spectral subtraction, log-MMSE, test-PSC and p-mmse in presence of babble, pink, white, f-16 and Volvo car interior noise. The noisy input speech signals are processed at various levels of input SNR range from ?5 to ?25 dB. Performance of the proposed method is evaluated with the help of PESQ, SNR and cepstral distance measure. The results obtained by proposed binary mask thresholding function based coiflet5 wavelet transform method are very encouraging and shows that the proposed method is much helpful in Arabic speech enhancement than other existing methods.     

OM-LSA和小波阈值去噪结合的语音增强

针对OM-LSA(optimally modified log-spectral amplitude estimator)算法产生的残留噪声,提出了一种结合OM-LSA和小波阈值去噪的语音增强算法。首先,进行语音对数幅度谱估计;然后,估计残留噪声,利用带噪语音第一级小波系数和语音不存在时的增益函数进行估计,解决了常规方法对增强后语音噪声估计不准确的问题;最后,在小波域利用软阈值法对语音信号进行阈值处理。实验结果表明,提出的算法有效地去除了OM-LSA算法中的残余噪声,在分段信噪比(segmental signal-to-noise ratio,SegSNR)和对数谱失真(log-spectral distortion,LSD)等指标评价上有较大的提高。     

Speech enhancement using Teager energy operated ERB-like perceptual wavelet packet decomposition

In recent past, wavelet packet (WP) based speech enhancement techniques have been gaining popularity due to their inherent nature of noise minimization. WP based techniques appeared as more robust and efficient than short-time Fourier transform based methods. In the present work, a speech enhancement method using Teager energy operated equal rectangular bandwidth (ERB)-like WP decomposition has been proposed. Twenty four sub-band perceptual wavelet packet decomposition (PWPD) structure is implemented according to the auditory ERB scale. ERB scale based decomposition structure is used because the central frequency of the ERB scale distribution is similar to the frequency response of the human cochlea. Teager energy operator is applied to estimate the threshold value for the PWPD coefficients. Lastly, Wiener filtering is applied to remove the low frequency noise before final reconstruction stage. The proposed method has been applied to evaluate the Hindi sentences database, corrupted with six noise conditions. The proposed method’s performance is analysed with respect to several speech quality parameters and output signal to noise ratio levels. Performance indicates that the proposed technique outperforms some traditional speech enhancement algorithms at all SNR levels.     

基于子带谱熵的仿生小波语音增强

前端噪声处理直接关系着语音识别的准确性和稳定性,针对小波去噪算法所分离出的信号不是原始信号的最佳估计,提出一种基于子带谱熵的仿生小波变换(BWT)去噪算法。充分利用子带谱熵端点检测的精确性,区分含噪语音部分和噪声部分,实时更新仿生小波变换中的阈值,精确地区分出噪声信号小波系数,达到语音增强目的。实验结果表明,提出的基于子带谱熵的仿生小波语音增强方法与维纳滤波方法相比,信噪比(SNR)平均提高约8%,所提方法对噪声环境下语音信号有显著的增强效果。     

基于感知掩蔽深度神经网络的单通道语音增强方法

本文将心理声学掩蔽特性应用于基于深度神经网络（Deep neural network,DNN）的单通道语音增强任务中,提出了一种具有感知掩蔽特性的DNN结构.首先,提出的DNN对带噪语音幅度谱特征进行训练并分别得到纯净语音和噪声的幅度谱估计.其次,利用估计的纯净语音幅度谱计算噪声掩蔽阈值.然后,将噪声掩蔽阈值和估计的噪声幅度谱联合计算得到一个感知增益函数.最后,利用感知增益函数从带噪语音幅度谱中估计出增强语音幅度谱.在TIMIT数据库上,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语音的训练集中出现,所提出的感知掩蔽DNN都能够在有效去除噪声的同时保持较小的语音失真,增强效果明显优于常见的DNN增强方法以及NMF（Nonnegative matrix factorization）增强方法.     

基于小波变换的语音增强方法研究

分析了小波去噪原理,根据随机噪声的小波变换系数在不同尺度上的传递特性和噪声信号奇异性与小波模极大值的关系,同时考虑到语音中浊音和清音的特点,提出了一种改进阈值的小波域语音增强方法。在阈值函数中引入参数,通过调整参数以获得最佳的小波系数的阈值估计,使得改进阈值介于硬阈值与软阈值之间。利用改进阈值对染噪语音信号的小波系数进行阈值处理,既抑制了噪声,又减少了语音段信息的损失。仿真结果表明,这是一种有效的语音增强方法。     

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.     

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。     

小波语音增强算法在说话人识别中的应用

干净语音环境下识别率很高的说话人识别系统,在有噪声语音环境下识别性能显著降低。针对这一问题,将小波语音增强算法应用于说话人识别系统,提出一种结点阈值去噪新方法。语音增强主要目的是从带噪语音中尽可能地提取纯净的原始语音。在不同信噪比条件下进行实验,结果表明,提出的方法比传统的阈值法能更好地提高语音质量。     

Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients

This paper describes a new method for contrast enhancement in images and image sequences of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means of a shrinkage function, thus preventing noise amplification. The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy imagery, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output data.     

小波阈值降噪算法中最优分解层数的自适应选择

小波阚值降噪算法是一种去除数字信号中白噪声的有效算法．针对加性高斯白噪声的情况,提出一种自适应小波降噪算法,用于语音信号的增强．它能根据带噪信号的特点,自适应选择小波变换的最优分解层数．实验结果表明,该算法比经典的小波降噪算法具有更好的降噪效果,能有效提高算法的实用性能．     

一种基于仿生小波变换的语音增强方法

提出了一种新的基于仿生小波变换的语音增强方法。该方法通过对仿生小波变换系数进行阈值处理,从而达到语音增强的目的。实验结果表明：该方法在四种实际噪声环境下均优于一些经典方法如：谱减法、维纳滤波和基于离散小波变换的阈值去噪方法,具有更好的语音增强效果。     

一种时间自适应阈值的小波包改进语音增强算法

针对传统小波语音增强算法存在过度周值处理的问题,提出一种改进的时间自适应阈值小波包去噪算法.该方法采用听觉感知小波包对噪声语音进行分解,得到小波包听觉感知节点上的系数,并基于语音存在概率估计按帧自动调节去噪周值,因改进的闲值能更好地避免语音小波包系数被过度阈值处理的情况,从而在抑制噪声的同时保留了更多的原始语音成分,进一步提高了降噪效果,实验结果表明,该算法比常规小波自适应闻值算法能得到更清晰的语音增强信号.     

Audio enhancement using local SNR-based sparse binary mask estimation and spectral imputation

This paper proposes a method for enhancing speech and/or audio quality under noisy conditions. The proposed method first estimates the local signal-to-noise ratio (SNR) of the noisy input signal via sparse non-negative matrix factorization (SNMF). Next, a sparse binary mask (SBM) is proposed that separates the audio signal from the noise by measuring the sparsity of the pool of local SNRs from the adjacent frequency bands of the current and several previous frames. However, some spectral gaps remain across frequency bands after applying the binary masks, which distorts the separated audio signal due to spectral discontinuity. Thus, a spectral imputation technique is used to fill the empty spectrum of the frequency band where it is removed by the SBM. Spectral imputation is conducted by online learning NMF with the spectra of the neighboring non-overlapped frequency bands and their local sparsity. The effectiveness of the proposed enhancement method is demonstrated on two different tasks use speech and musical content, respectively. Consequently, objective measurements and subjective listening tests show that the proposed method outperforms conventional speech and audio enhancement methods, such as SNMF-based alternatives and deep recurrent neural networks for speech enhancement, block thresholding, and a commercially available software tool for audio enhancement.     

基于改进的小波阈值函数语音增强方法

传统的小波阈值去噪方法会造成有用语音信号的损失, 信噪比改善情况不理想. 通过分析小波去噪原理, 提出了一种改进的小波阈值函数语音增强方法. 该方法结合小波软、硬阈值函数去噪的优点, 克服了硬阈值函数的不连续及软阈值函数存在偏差的缺点. 该方法首先对清浊音信号进行判断, 接着采用变化的阈值对清浊音信号的小波系数进行不同的阈值处理. 仿真实验结果表明, 改进的方法非常适用于强噪声背景下的语音增强, 无论在保留含噪语音信号中的清音信息, 还是在信噪比改善指标上均优于传统的软阈值法、谱减法和听觉感知小波变换法.     

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。     

An improved wavelet-based speech enhancement by using speech signal features

This paper presents a new speech enhancement system that works in wavelet domain. The core of system is an improved WaveShrink module. First, different parameters of WaveShrink are studied; then, based on the features of speech signal, an improved wavelet-based speech enhancement system is proposed. The system uses a novel thresholding algorithm, and introduces a new method for threshold selection. Moreover, the efficiency of system has been increased by selecting more suitable parameters for voiced, unvoiced and silence regions, separately. The proposed system has been evaluated on different sentences under various noise conditions. The results show a plausible improvement in performance of system, in comparison with similar approaches.