基于人类听觉系统的多频带语音增强

提出了一种基于人类听觉系统的多频带非线性谱减法来进行语音增强。根据人耳听觉特性,将含噪语音信号分在24个临界频带内,由各频带的不同信噪比来确定对应的谱减参数值。实验结果证明,在相同实验条件下,与功率谱减法（PSS）、非线性谱减法（NSS）和传统多频带谱减法（MBSS）相比,该方法增强后的语音信号具有更高的输出信噪比;能更好地消除背景噪声,抑制残留噪声;增强后的语音具有更好的可懂度和清晰度。     

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.     

一种基于听觉特性的自适应小波语音增强方法

提出一种基于人类听觉特性的自适应小波滤波算法。该方法用听觉感知小波变换对含噪语音信号进行小波分解,这样可以保证对信号频率和幅值的听觉特性,将经听觉感知小波变换所分离出来的噪声成分作为自适应滤波器的输入。通过采用递推最小二乘算法从而实现信噪分离的最佳滤波,以保证去除信号中的相关噪声。结果表明,该方法能实现非平稳信号在同频段对噪声成分和有用信号的最佳估计,提高了语音的清晰度和可懂度。     

A computational auditory scene analysis system for speech segregation and robust speech recognition

A conventional automatic speech recognizer does not perform well in the presence of multiple sound sources, while human listeners are able to segregate and recognize a signal of interest through auditory scene analysis. We present a computational auditory scene analysis system for separating and recognizing target speech in the presence of competing speech or noise. We estimate, in two stages, the ideal binary time–frequency (T–F) mask which retains the mixture in a local T–F unit if and only if the target is stronger than the interference within the unit. In the first stage, we use harmonicity to segregate the voiced portions of individual sources in each time frame based on multipitch tracking. Additionally, unvoiced portions are segmented based on an onset/offset analysis. In the second stage, speaker characteristics are used to group the T–F units across time frames. The resulting masks are used in an uncertainty decoding framework for automatic speech recognition. We evaluate our system on a speech separation challenge and show that our system yields substantial improvement over the baseline performance.     

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。     

一种基于鲁棒局部纹理特征的背景差分方法

针对复杂场景下运动目标的精确检测这一问题,提出一种对噪声鲁棒并具备灰度尺度不变性的局部纹理特征描述子LBP_Center,将其与像素的颜色信息结合应用于背景建模中,采用随机抽样的机制更新模型,同时引入背景复杂度以去除多模态动态背景产生的噪点。在标准测试数据集上的实验结果表明,该算法对柔性阴影及光照缓慢变化具备良好的鲁棒性,综合性能更优。     

利用递归平均和谱减技术的语音增强方法

提出了一种基于改进的谱减法的语音增强算法。该算法首先利用了一种由最小值控制的递归平均的噪声谱估计算法,因而无需语音端点检测,其次利用一种通过递归计算得到的基于子带信噪比的过减因子,减小了产生“音乐噪声”的可能性。分析和实验表明,提出的算法对“音乐噪声”起到了一定的抑制效果,并有效地提高了输出信噪比。     

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。     

A new structural approach in system identification with generalized analysis-by-synthesis for robust speech coding

In this paper, we apply a new structural approach to generalized analysis-by-synthesis (GAbS) for system identification as a preprocessor of a low-bit-rate speech coder. In our approach, the coder-decoder (CODEC) system is separately estimated and then applied to modify the current input signal. This is different from that originally proposed where the CODEC system is sequentially estimated and then applied to the next input signal. The proposed estimation scheme is compared to the conventional method in terms of the signal modification approach under the various noise data and in several SNR conditions, and shows better performance.     

基于多通道小波变换的化学信号滤噪方法

基于多小波变换的理论与算法,提出了多小波软阈值去噪算法。用模拟高斯信号对多小波软阈值滤噪方法与单小波软阈值滤噪方法进行了比较,实验结果表明,多小波滤噪方法去噪效果优于单小波。将多小波软阈值滤噪方法用于黄连提取物的5种组分毛细管电泳信号的滤噪,进行滤噪处理后,噪音基本上被消除,峰位置十分清晰,峰的位置、面积及高度基本不变,基线平稳,有利于进一步进行定量计算。     

A novel method of medical image enhancement based on wavelet decomposition

The efficiency of image enhancement algorithms depends on the quality and processing speed of image enhancement. There are many algorithms to implement image enhancement using wavelet theory. These algorithms have one thing in common: they all capture image details by decomposing low frequency sub-images. In fact, a lot of details in high-frequency sub-images are also found. Enlightened by the above-mentioned facts, a novel medical image enhancement method based on wavelet decomposition is proposed by adding details from the high-frequency sub-images and decomposing the image specially with ant-symmetric biorthogonal wavelet instead of some traditional wavelets. It not only improves the image enhancement, but also overcomes the shortcomings of large computation with faster computational speed and satisfies the real-time requirement in edge detection. Simulation experiments of mammographic images are implemented by Matlab with several different methods, the results show that the proposed method is superior to some popular methods, such as histogram equalization and wavelet nonlinear enhancement.     

A speech enhancement method based on sparse reconstruction of power spectral density

Using sparse representation of power spectral density (PSD) approximated by magnitude-squared spectrum, a new speech enhancement method is presented. The approximation K-singular value decomposition (K-SVD) algorithm with nonnegative constraint is used to train an overcomplete dictionary of the clean speech PSD. The least angle regression algorithm (LARS) with a termination rule based on the _?2${?}_2$ norm of the sum of the noise PSD and cross term between the clean speech and noise spectra is applied to estimate the clean speech PSD. Combining the estimated PSD with the signal subspace approach based on the short-time spectral amplitude (SSB-STSA), the enhanced speech signal is obtained. The simulation results show that the new method can yield better performance in most of noise conditions.     

基于DFRCT滤波器组自适应语音增强算法

提出一种改进的语音增强方法,将带噪语音信号进行子带分解,再对子带信号进行离散分数余弦变换（DFRCT）域滤波,利用了DFRCT良好的正交特性,且自适应滤波采用最小均方（LMS）算法。对滤波后的信号进行DFRCT逆变换得到增强后的子带语音信号,合成增强后的语音信号。仿真结果表明,该算法在减少输入信号自相关程度的基础上,提高了收敛速度,减少了计算时间（约10 s）,增强后的语音信号的分段信噪比（SegSNR）和PESQ值都有所提高,具有良好的语音增强效果。     

基于小波变换多分辨率特性的语音消噪方法的研究

小波分析是一种信号的时间——尺度分析方法,特别适合于非平稳信号的分析,具有多分辨率分析的特性,而且在时频两域都具有表征信号局部特征的能力。通过分析语音信号的特性,利用小波变换的多分辨率分析特性,提出了首先对信号进行清浊音判断,其次运用多尺度多闽值方法来抑制包含有噪声的语音信号在不同尺度上的噪声小波系数,从而实现在重构语音信号中消噪的目的,并通过计算机仿真结果验证了该方法的有效性。     

双树复小波包变换语音增强新算法

实小波包变换是语音增强中效果较好的一种算法,利用阈值的方法对小波包系数进行压缩进而重构语音信号.分析了实小波包变换的平移敏感性,以及其对语音进行增强时的缺陷.提出采用双树复小波包变换方法进行语音增强,当低通滤波器和高通滤波器对应的小波基近似为希尔伯特变换对时,该变换能大大减小实小波包变换中的平移敏感性.同时考虑小波包系数之间的相互关系,提出了重叠块复阈值算法.结果表明,算法优于传统实小波包变换及点阈值算法,尤其对含周期噪声的语音信号,双树复小波包变换算法的优势更为明显.     

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。     

基于小波特征提取的语音监测系统

论文针对小波变换和语音信号的特点,把小波变换和形态滤波法结合应用于语音信号基音周期的提取,并在此基础上把小波变换和说话人声道特征参数相结合,用于声道特征的提取。最后在以上研究的基础上设计了一种用于公安侦破和司法鉴定的语音监测系统。     

Gabor滤波在方向上的二值化指纹增强

指纹图像增强是指纹特征提取和识别中的难点之一.本文介绍了一种在Gabor滤波基础上,基于方向图的、具有动态阈值的指纹图像二值化方法.该方法充分利用了指纹图像本身方向和灰度变化的特点,在保持指纹特征基本不丢失的情况下,可直接从指纹源图像中得到二值化图像,完成一般图像处理中的平滑、增强、二值化的过程.实验表明,此方法对于低质量的图像有很好的效果.     

A probabilistic wavelet system for stochastic and incomplete data-based modeling.

A probabilistic wavelet system (PWS) is proposed to model the unknown dynamic system with stochastic and incomplete data. When compared with the traditional wavelet system, the PWS uses a novel three-domain wavelet function to make a balance among the probability, time, and frequency domains, which achieves a robust modeling performance with poor data information. The definition, transformation, multiple-resolution analysis, and implementation of the PWS are presented to construct the whole theoretical framework. Simulation studies show that the performance of the proposed PWS is superior to the traditional one in a stochastic and incomplete data environment.