A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments

In this paper, we propose a speech enhancement method where the front-end decomposition of the input speech is performed by temporally processing using a filterbank. The proposed method incorporates a perceptually motivated stationary wavelet packet filterbank (PM-SWPFB) and an improved spectral over-subtraction (I-SOS) algorithm for the enhancement of speech in various noise environments. The stationary wavelet packet transform (SWPT) is a shift invariant transform. The PM-SWPFB is obtained by selecting the stationary wavelet packet tree in such a manner that it matches closely the non-linear resolution of the critical band structure of the psychoacoustic model. After the decomposition of the input speech, the I-SOS algorithm is applied in each subband, separately for the estimation of speech. The I-SOS uses a continuous noise estimation approach and estimate noise power from each subband without the need of explicit speech silence detection. The subband noise power is estimated and updated by adaptively smoothing the noisy signal power. The smoothing parameter in each subband is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement method is tested on speech signals degraded by various real-world noises. Using objective speech quality measures (SNR, segmental SNR (SegSNR), perceptual evaluation of speech quality (PESQ) score), and spectrograms with informal listening tests, we show that the proposed speech enhancement method outperforms than the spectral subtractive-type algorithms and improves quality and intelligibility of the enhanced speech.     

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。     

基于小波熵自适应阈值的语音信号去噪新方法

针对语音信号去噪问题, 提出小波熵自适应阈值去噪法。首先利用小波变换分解带噪语音信号, 计算小波分解后信号子带区间的小波熵, 然后将小波熵和自适应阈值相结合确定各层高频系数的阈值门限, 采用折中指数阈值函数对各层高频系数进行去噪处理, 重构降噪后的语音信号, 最后对比小波熵自适应阈值、极大极小阈值、固定阈值和无偏风险阈值去噪方法的性能。实验结果表明, 当输入信噪比为5 dB时, 小波熵自适应阈值去噪法的输出信噪比是最大的, 且其输入输出信噪比曲线高于其他三种阈值去噪法的输入输出信噪比曲线, 从而证实该算法具有更好的去噪性能。     

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。     

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.     

Multi notch adaptive digital filter design for enhancement of speech signals embedded in non-stationary noise

This paper presents a new approach to speech enhancement based on modified least mean square-multi notch adaptive digital filter (MNADF). This approach differs from traditional speech enhancement methods since no a priori knowledge of the noise source statistics is required. Specifically, the proposed method is applied to the case where speech quality and intelligibility deteriorates in the presence of background noise. Speech coders and automatic speech recognition systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The proposed method uses a primary input containing the corrupted speech signal and a reference input containing noise only. The new computationally efficient algorithm is developed here based on tracking significant frequencies of the noise and implementing MNADF at those frequencies. To track frequencies of the noise time-frequency analysis method such as short time frequency transform is used. Different types of noises from Noisex-92 database are used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR) as well as subjective listing test demonstrate consistently superior enhancement performance of the proposed method over tradition speech enhancement method such as spectral subtraction.     

一种Bark子波变换的电子耳蜗语音增强算法

提出了一种Bark子波变换的电子耳蜗语音增强算法。该算法首先引入与人耳听觉系统更为适应的Bark子波变换来进行电子耳蜗CIS语音信号处理,然后在每个Bark通道中利用非线性谱减法对其进行语音增强,谱减法的参数由人耳隐蔽阈值来控制。结果表明：即使在低信噪比的情况下,信噪比也能提高16 dB左右,合成的语音对于电子耳蜗使用者具有较好的清晰度和可懂度。     

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.     

一种基于小波包变换加权自相关的基音检测算法

噪声环境下的基音检测在语音信号处理中占有重要地位。为了有效提取低信噪比情况下的语音基音周期,提出了一种基于小波包变换加权线性预测自相关的检测方法。该方法首先利用小波包自适应阈值消除噪声,将多级小波包变换的近似分量求和以突出基音信息,并采用小波包系数加权线性预测误差自相关的方法突出基音周期处的峰值,提高了基音周期检测的精度。实验结果表明,与传统的自相关法、小波加权自相关法相比,该方法鲁棒性好,基音轨迹平滑,具有更高的准确性,即使在信噪比为-5dB时仍能取得较为理想的结果。     

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。     

基于小波变换和Kalman滤波的语音增强方法

针对受加性噪声干扰的语音信号,采用基于小波变换的Kalman滤波方法,提出一种有效的语音增强方法.分析在实际处理中所遇到的二进小波变换、滤波参数估计、Kalman滤波发散等问题.语音增强的效果采用信噪比来进行评估.仿真实验表明在加性噪声为高斯白噪声和色噪的情况下,该方法均具有较好的有效性.     

基于遗传算法的仿生小波语音增强

分析遗传算法和仿生小波变换的原理和方法,提出一种基于遗传算法的仿生小波语音增强算法.首先将普通小波变换转换为仿生小波变换,得到仿生小波变换系数,接着利用遗传算法的选择、交叉、变异获得仿生小波的优化阈值参数,从而确定最优小波阈值,随后结合最优小波阈值和改进阈值函数去噪,最终将经阈值处理后的仿生小波的系数变换至普通小波域且实行连续小波逆变换,获得增强的语音信号.仿真结果表明,在低信噪比环境下,与传统的最小统计和仿生小波变换算法相比较,经本文提出的算法处理后的增强语音其失真和残余噪声更小,语音质量和可懂度都较高.     

基于多重小波变换与新阈值函数的去噪方法研究*

基于小波分析的特点,提出了一种对信号数据进行多重小波变换阈值去噪的方法。新的阈值函数的构造是在研究D.L.Donoho和I.M.Johnstone提出的小波阈值去噪方法的基础上完成的。与传统的软硬阈值函数相比,新阈值函数在整个定义域内统一定义,表达式简单易于计算,与软阈值函数一样具有连续性,而且是高阶可导的,便于进行各种数学处理,克服了硬阈值函数不连续、软阈值函数中估计小波系数与分解小波系数之间存在着恒定偏差的缺点。仿真实验结果表明,采用新的阈值函数的去噪效果在信噪比增益和最小均方误差意义上均优于传统的软     

Speech enhancement based on stationary bionic wavelet transform and maximum a posterior estimator of magnitude-squared spectrum

Numerous efforts have focused on the problem of reducing the impact of noise on the performance of various speech systems such as speech coding, speech recognition and speaker recognition. These approaches consider alternative speech features, improved speech modeling, or alternative training for acoustic speech models. In this paper, we propose a new speech enhancement technique, which integrates a new proposed wavelet transform which we call stationary bionic wavelet transform (SBWT) and the maximum a posterior estimator of magnitude-squared spectrum (MSS-MAP). The SBWT is introduced in order to solve the problem of the perfect reconstruction associated with the bionic wavelet transform. The MSS-MAP estimation was used for estimation of speech in the SBWT domain. The experiments were conducted for various noise types and different speech signals. The results of the proposed technique were compared with those of other popular methods such as Wiener filtering and MSS-MAP estimation in frequency domain. To test the performance of the proposed speech enhancement system, four objective quality measurement tests [signal to noise ratio (SNR), segmental SNR, Itakura–Saito distance and perceptual evaluation of speech quality] were conducted for various noise types and SNRs. Experimental results and objective quality measurement test results proved the performance of the proposed speech enhancement technique. It provided sufficient noise reduction and good intelligibility and perceptual quality, without causing considerable signal distortion and musical background noise.     

小波消噪和ICA在语音信号分离中的应用

为了消除语音信号分离中仍存在的部分混叠声音,提出一种基于小波消噪和独立分量分析（ICA）结合的信号分离方法。该方法将小波变换和独立分量分析结合,利用小波变换的去噪作用,滤除原始语音信号中的噪声后作为ICA的输入信号,采用FastICA算法在小波域进行独立分量分析,对输入信号实施分离。实验结果表明,该方法大大调高了传统独立分量分析对语音信号的分离效果。     

盲源分离在单通道语音增强算法中的应用

提出一种单通道语音增强算法。首先由接收到的单声道语音信号的含噪部分构造一个假想噪声源,将这一噪声源和含噪的信号作为多通道自适应去相关（MAD）盲分离算法的输入,得到增强的语音信号。进一步将这一增强的语音作为输入,利用Daubechies小波对其进行分解,在小波域中选取合适的阈值函数进行滤波,然后合成时域语音信号。根据以上步骤得到的增强语音有较高的信噪比及可懂度。     

基于前置滤波和小波变换的带噪语音基音周期检测方法

根据语音信号的基音周期范围有限和在声门闭合时刻语音信号出现锐变的特点,提出一种基于前置滤波和小波变换的基音周期检测方法。带噪语音信号经过3阶椭圆低通滤波器滤波后,采用以二次样条小波作为小波函数,进行一级小波变换检测语音信号的锐变点,再计算基音周期。实验表明,本文提出的基音周期检测方法,与平均幅度差函数(AMDF)和自相关函数(ACF)方法相比,提高了提取基音周期的准确率;与多尺度小波变换的基音周期检测方法相比,减小了计算量,削弱了噪声信号和语音的共振峰对基音周期检测的影响。     

Visual enhancement of digital ultrasound images using multiscale wavelet domain

Ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body. The main disadvantage of medical ultrasonography is the poor quality of images, which are affected by multiplicative speckle noise. In this paper, we present a novel method for despeckling medical ultrasound images. The primary goal of speckle reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of the wavelet transform and apply multi-resolution analysis to localize an image into different frequency components or useful subbands and then effectively reduce the speckle in the subbands according to the local statistics within the bands. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless. The objective of the paper is to investigate the proper selection of wavelet filters and thresholding schemes which yields optimal visual enhancement of ultrasound images, in particular. We employ the wavelet shrinkage denoising techniques with different wavelet bases and decomposition levels on the individual subbands to achieve the best acceptable speckle reduction while maintaining the fidelity of the image and also examine the effects of different thresholding techniques as well as shrinkage rules for denoising ultrasound images. The proposed method consists of the log transformed original ultrasound image being subjected to wavelet transform, which is then denoised by a thresholding technique using a shrinkage rule. Experimental results show that the subband decomposition of ultrasound images, using Bior6.8 and level 3 with soft thresholding based on Bayes shrinkage rule, performs better than other techniques. The performance is measured in terms of Variance, Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak SNR (PSNR) and Correlation Coefficient (CC). The results of wavelet shrinkage techniques are compared with common speckle filters. We observe that the proposed method achieves better visual enhancement of ultrasound images which would lead to more accurate image analysis by the medical experts.     

基于卷积神经网络的面罩语音识别

针对带噪面罩语音识别率低的问题,结合语音增强算法,对面罩语音进行噪声抑制处理,提高信噪比,在语音增强中提出了一种改进的维纳滤波法,通过谱熵法检测有话帧和无话帧来更新噪声功率谱,同时引入参数控制增益函数;提取面罩语音信号的Mel频率倒谱系数(MFCC)作为特征参数;通过卷积神经网络(CNN)进行训练和识别,并在每个池化层后经局部响应归一化(LRN)进行优化.实验结果表明:该识别系统能够在很大程度上提高带噪面罩语音的识别率.     

基于正交小波变换的自适应语音消噪改进方法

提出一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率．正交小波变换在自适应滤波中起到白化的作用，使自适应滤波器的输入正交化．通过正交小波分解，自适应滤波器中的信号能量降低，输入自相关矩阵的动态范围减小，特征值分布更加集中，从而使收敛速率加快．