二次离散小波变换的语音增强方法

提出了一种基于二次离散小波变换（DWT）的语音增强算法。该算法首先对带噪语音信号进行离散小波变换,提取离散细节信号,并对其进行第二次离散小波变换。再按照不同的规则选取阈值,对信号进行去噪处理。最后再对出来后的语音信号进行合并。对比实验结果表明,该方法具有良好的消除噪声的效果,提高了语音的清晰度和可懂度。     

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。     

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。     

Hilbert-Huang变换在语音去噪中的应用

研究语音信号噪声抑制问题,针对噪声污染干扰正确语音的传输,传统采用的HHT噪声抑制方法有多尺度滤波和阈值去噪,对所有的IMF分量进行处理,没有将IMF分量中的有用信号和噪声信号区别开来,去噪效果受到抑制.为使去噪效果更好,提出一种新的基于能量分析的阈值去噪方法,对含噪信号经过Hilbert-Huang变换后的IMF分量,对于信号和噪声能量分布的特点进行能量分析,将加噪信号中有用信号和噪声信号分离开,再利用阈值去噪方法完成去噪.通过仿真,可观察出语音信号的噪声得到了抑制,能够准确识别语音信号,并且比小波方法简单,不用选择小波基和确定分解层数,不用选择判断阈值,就能够达到或接近小波去噪的水平.     

基于平移不变小波的语音信号去噪研究

通过小波阈值方法可以去除语音中的噪声,但它的结果中会出现诸如Pesudo-Gibbs现象之类的情况.为消除此类情况,将平移不变量小波变换引入到语音信号去噪中,并结合阈值方法进行去噪处理.经过仿真实验,证明这种方一法比一般的阈值方法有很大改进,提高了信噪比.     

基于小波变换的数字水印隐藏与检测算法

主要研究了在数字语音信号中加入数字水印的方法,提出了一种基于小波变换的数字水印隐藏与检测算法,用这种算法隐藏水印具有很强的隐藏性,对原始语音的影响基本上察觉不出来。叠加了水印的语音在经过多种强干扰及各种信号处理的变换之后,使用本算法仍能正确检测出水印的存在,如它可以抵抗噪声干扰、去噪算法对信号进行去噪处理、语音信号的有损压缩以及信号的重新采样等。     

基于LabVIEW的UWT语音信号降噪处理

在LabVIEW软件平台下,应用UWT(非降采样小波变换)算法设计一新型处理软件,该软件集合了db5、coif5、sym5等三种不同正交小波的信号分解、阈值去噪和信号重构算法,并使用该软件成功地还原了一带噪语音信号,得出在强噪声背景下,该小波变换算法具有良好的保留语音原信号特性且降噪效果优越的结论。     

基于阈值的小波域语音降噪新算法

提出了一种新的基于阈值的小波域语音降噪算法。采用小波包对含噪语音进行分解，克服了传统的正交小波变换的缺陷。采用自适应阈值的方法，对每一尺度上的噪声最大量进行去噪，保留有用信号，可以进一步提高信噪比，仿真实验表明，该方法有更好的去噪效果。     

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.     

Speech transmission with COFDM based on different discrete transforms

Recently, the multimedia and cellular technologies have spread dramatically. Therefore, the demand for digital information has increased. Speech compression is one of the most effective forms of communication. This paper presents three approaches for the transmission of compressed speech signals over convolutional Coded Orthogonal Frequency Division Multiplexing (COFDM) system with a chaotic interleavering technique. The speech signal has is compressed using the Set Partitioning In Hierarchical trees (SPIHT) algorithm, which is an improved version of EZW and which is characterized by a simple and effective method for further compression. For mitigation of the fading due to multipath wireless channels, this paper proposes a COFDM system based on fractional Fourier transform (FrFT), a COFDM system based on discrete Cosine transform (DCT), and a COFDM system based on discrete wavelet transform (DWT). The FrFT has the ability of solving the frequency offset problem, which causes the received frequency-domain sub-carriers to be shifted, and therefore, the orthogonality between subcarriers deteriorates even with equalization. The DCT has an advantage of increased computational speed as only real calculations are required. The DWT is spectrally efficient since it does not utilize cyclic prefix (CP). These systems have been designed under the assumption that corruptive background noises are absent. Therefore, denoising techniques, namely wavelet denoising and Wiener filtering methods are suggested at the receiver to achieve enhancement in the speech quality. The simulation experiments shows that the proposed COFDM–DWT with Wiener filtering at the receiver has a better trade-off between BER, spectral efficiency and signal distortion. Hence, the BER performance is improved with small bandwidth occupancy. Moreover, due to the denoising stage, the speech quality is improved to achieve good intelligibility.     

基于级联离散小波变换的信号去噪方法研究

提出了基于级联离散小波变换的信号去噪方法。该方法通过对带噪信号作一层离散小波变换(DWT)后提取的低频部分和高频部分分别作一层DWT和四层DWT,然后,对低频部分提取的低频成分和高频成分均作三层DWT,接着,对所有分解的小波系数进行阈值处理,最后,完成信号重构。实验结果表明:在同样的小波分解层次下,本方法去噪效果好于DWT法和WPD法。     

基于小波域影响锥的小波萎缩去噪算法

信号的奇异点和不规则结构往往携带了一些重要的信息,而小波变换是检测信号奇异点和跳变沿的一种有效的工具。为了更好地从噪声污染的信号中消除干扰和保留信号原始特征,根据奇异点影响锥的范围和小波域模极大值线传播特性,提出了一种新的算法来寻找奇异点影响锥内的小波系数,并进行去噪处理。仿真实验表明：与传统的阈值去噪方法相比,该算法实现简单并且有着更好的滤波性能。     

A Wavelet Packet Tree Denoising Algorithm for Images of Atomic‐Force Microscopy

A threshold‐free denoising procedure of acquired discrete Atomic‐force microscopy (AFM) signals using the discrete wavelet transform (DWT) method is presented in this article. The integration of a denoising procedure into a control structure is extremely important for each kind of system to be controlled. The detection of unavoidable measurement noise in the acquired data of the AFM signal is done by using orthogonal wavelets (Daubechies and Symmlet) and with different polynomial approximation order for each family. The proposed denoising algorithm, based on the free wavelet toolboxes from the WaveLab 850 library of the Stanford University (USA), compares the usefulness of Daubechies and Symmlet wavelets with different vanishing moments. With the help of a seminorm the noise of a sequence is defined as a coherent and incoherent part of the AFM signal. In the first step of the procedure the algorithm analyzes the frequency subspaces of the wavelet packets tree and searches for small or opposing components in the wavelet domains. In the second step of the procedure the incoherent components in the low‐ and high frequency domains are localized and the incoherent is then removed from the AFM signal. The proposed algorithm structure is used to improve the quality of the AFM signals and it can be easily integrated into the existing AFM control hard‐ and software structures. The effectiveness of the proposed denoising algorithm is validated with real measurements.     

基于离散小波变换的信号分解与重构

为数值计算简化和理论分析简便,在实际信号处理应用中,需要对小波变换进行离散化处理。介绍了傅里叶变换与小波变换的基本理论,以及离散小波变换在信号分解和重构过程中的原理及方法。利用MATLAB小波工具箱中提供的函数分别对一维信号和语音信号进行分解与完全重构,并对结果进行分析比较。仿真结果表明,用离散小波变换进行一维和语音信号分解时均可有效地获取其平均相似信息和细节信息,重构信号与原始信号相比损失较少,分解和重构均得到了很好的效果。     

Wavelet based speech presence probability estimator for speech enhancement

A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches.     

带噪语音信号小波去噪算法研究

去噪问题是信号处理中必不可少的问题。滤波作为传统的去噪方法,主要包括线性滤波和非线性滤波。噪声与信号频率重叠,传统方法要想取得良好的去噪效果必须牺牲部分信号。现有的小波模极大值去噪方法虽然有较好的去噪效果,但是该方法计算量大。小波阈值去噪方法更简单,但小波重构后的小波系数与噪声的小波系数存在恒定的偏差。在阈值去噪方法的基础上提出一种改进算法,仿真结果表明该算法在加性高斯白噪声污染下表现出较好去噪效果。     

一种基于小波变换的导弹运输车辆故障诊断方法

利用离散小波变换的时频特性和连续小波变换检测信号边沿的原理,进行模拟导弹运输 车辆轮胎和板簧的故障检测、分离和定位.该方法不需要对象的数学模型.模型车的故障诊断实验 结果表明.该方法灵敏度高,对噪声具有较好的鲁棒性.