基于多频段排序的语音卷积混合盲源分离

针对语音卷积盲源分离频域法排列顺序不确定性问题,提出一种多频段能量排序算法。首先,通过对混合信号的短时傅立叶变换(STFT),在频域上各个频点建立一个瞬时混合模型进行独立分量分析,之后结合能量相关排序法和波达方向(DOA)排序法解决排序不确定性问题,再利用分裂语谱方法解决幅度不确定性问题,进而得到每个频点正确的分离子信号,最后利用逆短时傅立叶(ISTFT)变换得到分离的源信号。仿真结果表明,与Murata的排序算法对比,改进的算法在信号偏差比、信道干扰比、系统误差比上都所提高。     

改进频点权重系数的频域盲源分离排序算法

为解决卷积混合频域盲源分离排序不确定问题,研究了分离矩阵行列式变化和频点距离对基于相邻频点幅度相关性排序算法的影响,提出了改进的盲源分离排序算法.改进算法用权重系数来衡量频点对排序的影响,并将分离矩阵作为下一频点分离矩阵的迭代初值,给出了权重系数设定函数.最后对瞬时混合信号、卷积混合信号、实际房间采集信号分别进行盲源分离实验.实验结果表明,与Murata算法相比,改进算法分离信号信噪比提高、分离速度加快、算法鲁棒性强.     

基于改进时频比的语音音乐信号分离

时频比是混合信号在时频域幅值特性的比值,利用时频比寻找混合信号中的单源点,对相应的比值构成的矩阵求逆可以得到对源信号的估计。针对基于时频比的盲源分离将信号变换到时频域后计算量大且对算法有效的时频窗较少的问题,提出用重复结构周期内的时频点代替整个时频域进行单源点的检测,重复结构内的时频点在每个周期内都有相似的值,通过减少一个周期内时频点的检测,由单源点对应的时频比恢复出源信号。用相似系数矩阵评价分离效果,仿真实验结果表明,在达到几乎相同的相似系数的情况下,运行时间可减少45.43%,可有效降低运算量。     

卷积混合盲源分离中1种改进的排序模糊消除算法

频域方法可以有效地解决卷积混合盲源分离问题.针对频域方法中存在排序模糊,基于分离信号相邻频点功率谱密度的相关性较高的原理,提出1种改进的排序模糊消除算法.相比于原算法,扩展了参考频点的取值范围,同时还采用了1种置信度量方法,能够获得更准确的排序估计.仿真实验表明所提算法有效地消除了排序模糊,并且能够纠正某一频点排序的突...     

基于时频分布的非平稳信号盲分离算法

针对传统的盲源分离方法往往忽略信号非平稳性的问题,基于从瞬时线性混合模型的观测信号中分离出相互独立的源信号,并针对信号具有非平稳性,结合时频分析和盲源分离各自的特点,对非平稳信号盲分离进行了研究,并提出了一种新的具有不同空间时频分布的非平稳盲分离算法.仿真实验表明,通过采用维纳全时频域搜索来寻找局部最大值的平滑伪Wigner-Ville分布,该算法可以抑制交叉项而且能够保持时频聚集性,并达到了很好的分离效果.     

基于修正离散傅里叶变换的频域卷积混合盲分离

针对频域卷积混合盲分离,依据所导出的卷积混合信号每帧的频域表示模型,提出了一种最小均方误差意义下的最优变换--修正离散傅里叶变换,用于代替频域卷积混合盲分离中常用的离散傅里叶变换.在每个频率片上,卷积混合信号的修正离散傅里叶变换系数在最小均方误差意义下最接近于源信号频谱的瞬时混合.相对于离散傅里叶变换系数,现有瞬时混合盲分离算法能从修正离散傅里叶变抉系数中更精确地估计各频率片上分离矩阵,从而提高现有频域卷积混合盲分离算法的分离性能.仿真结果证明了修正离散傅里叶变换对现有频域卷积混合盲分离算法的有效性.     

基于平行因子分解的频域盲解卷积算法

针对卷积混合盲分离问题,文章提出了一张基于张量平行因子分解的盲分离算法。该算法通过将接收信号的频域相关矩阵叠加成三阶张量,再对此三阶张量进行平行因子分解,最后利用基于K-means聚类的全排列解模糊算法来完成无排列模糊的混合矩阵估计。通过仿真实验,计算分离信号与源信号的相似系数,结果表明提出的算法具有很好的分离效果,而且实现简单,可满足实际应用的要求。     

基于新型混合模型的欠定盲分离方法

针对欠定盲分离问题,提出了一种新的源恢复方法. 在时频域局部区域采用复高斯分布对源信号进行建模,将语音信号的稀疏性和局部平稳性结合在一起,提出了一种新的混合模型来描述观测信号在局部区域的概率分布.通过该模型,将每个时频点的源信号状态的判断问题转换成模型的参数估计和后验概率的计算问题,最后通过子混合矩阵的逆恢复出源信号. 实验结果表明,该方法具有很快的收敛速度,并且比已有方法具有更好的分离性能.     

频域盲解卷积局限性分析及一种改进算法

在频域盲解卷积问题中,时域信号的卷积混合转化为频域信号在有限频点的瞬时混合,使算法复杂度大大降低。但这种算法的局限是分离结果存在次序和幅度上的不确定性,并且窗函数长度和信号非平稳性之间存在相互制约的关系。文中对语音信号频域盲解卷积算法存在的制约因素进行分析并提出一种改进的基于包络相关性的排序方法。在分裂谱法的基础上,通过“分裂”后的多路信号求得“总包络”,再依据“总包络”进行排序,从而克服传统的直接依据输出信号包络相关性进行排序的不足。实验结果表明,采用本方法可获得较高的分离质量。     

频域盲解卷积局限性分析及一种改进算法

在频域盲解卷积问题中,时域信号的卷积混合转化为频域信号在有限频点的瞬时混合,使算法复杂度大大降低.但这种算法的局限是分离结果存在次序和幅度上的不确定性,并且窗函数长度和信号非平稳性之间存在相互制约的关系.文中对语音信号频域盲解卷积算法存在的制约因素进行分析并提出一种改进的基于包络相关性的排序方法.在分裂谱法的基础上,通过"分裂"后的多路信号求得"总包络",再依据"总包络"进行排序,从而克服传统的直接依据输出信号包络相关性进行排序的不足.实验结果表明,采用本方法可获得较高的分离质量.     

多速率STFT超宽带信号瞬时频率估计研究

提出多速率短时傅里叶变换(Multi Rate Short Time Fourier Transform,MR-STFT)瞬时频率估计算法,提高了超宽带信号瞬时频率估计精度;该方法将多速率信号处理算法与短时傅里叶变换(STFT)技术相结合,兼顾采样频率和被测频率,将宽频范围进行分段采样,对分段处理结果进行拟合,构成多速率STFT算法,实现超宽带信号瞬时频率的高精度测量;通过对仿真信号和实测信号进行处理,研究了方法的可行性和频率估计精度,结果表明MR-STFT算法较大提高了超宽带信号瞬时频率估计精度,尤其对低信噪比的超宽带信号效果显著。     

基于自动搜峰和shannon熵的车辆轴承多普勒畸变故障声信号校正研究

针对传声器采集的运动声源信号存在多普勒畸变问题,提出一种基于自动搜峰和shannon熵的滚动轴承多普勒畸变故障声信号校正方法。首先对所采集的声音信号进行短时傅里叶(STFT)时频分析;然后利用自动搜峰方法进行瞬时频率估计,设置shannon熵来提高瞬时频率估计精度,并得到拟合的瞬时频率曲线,进而得到信号重采样时间点;最后对原信号进行时域重采样,从而使畸变信号得以矫正。通过仿真和动态滚动轴承内外圈故障声信号的实验验证了此种方法的可行性。     

基于局部多项式傅里叶变换的多分量线性调频信号瞬时频率估计*

针对多分量线性调频信号的瞬时频率估计问题,把局部多项式傅里叶变换应用到求多分量线性调频信号瞬时频率估计中,提出了一种不受分量间交叉项干扰的新方法,该方法对多分量线性调频信号进行局部多项式傅里叶变换,把每一个线性调频信号分离出来,根据每一个线性调频信号的局部多项式傅里叶变换谱,通过搜索找到谱的峰值所在的位置,从而找到峰值所对应的频率,准确地估计出了多分量线性调频信号的瞬时频率。仿真实验中与多分量信号的Wigner-Hough变换进行了对比,验证了该方法的有效性。     

An unsupervised approach for co-channel speech separation using Hilbert–Huang transform and Fuzzy C-Means clustering

In this paper we discuss an unsupervised approach for co-channel speech separation where two speakers are speaking simultaneously over same channel. We propose a two stage separation process where the initial stage is based on empirical mode decomposition (EMD) and Hilbert transform generally known as Hilbert–Huang transform. EMD decomposes the mixed signal into oscillatory functions known as intrinsic mode functions. Hilbert transform is applied to find the instantaneous amplitudes and Fuzzy C-Means clustering is applied to group the speakers at initial stage. In second stage of separation speaker groups are transformed into time–frequency domain using short time Fourier transform (STFT). Time–frequency ratio’s are computed by dividing the STFT matrix of mixed speech signal and STFT matrix of stage1 recovered speech signals. Histogram of the ratios obtained can be used to estimate the ideal binary mask for each speaker. These masks are applied to the speech mixture and the underlying speakers are estimated. Masks are estimated from the speech mixture and helps in imputing the missing values after stage1 grouping of speakers. Results obtained show significant improvement in objective measures over other existing single-channel speech separation methods.     

A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter.     

数字测频算法研究综述

介绍了目前比较常用的几种数字测频算法及其基本原理,并对其中的直接计数法、相位推算法、频率推算法、傅里叶变换法及谱估计等方法进行了MATLAB仿真研究.总结了各种算法的主要优缺点,为设计者选取合适的算法进行工程应用提供了参考.     

Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation

We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura–Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and convolutive mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008).      

Controlled-coverage discrete S-transform (CC-DST): Theory and applications

The S-transform (ST) is a popular linear time-frequency (TF) transform with hybrid characteristics from the short-time Fourier transform (STFT) and the wavelet transform. It enables a multi-resolution TF analysis and returns globally referenced local phase information, but its expensive computational requirements often overshadow its other desirable features. In this paper, we develop a fully discrete ST (DST) with a controllable TF sampling scheme based on a filter-bank interpretation. The presented DST splits the analyzed signal into subband channels whose bandwidths increase progressively in a fully controllable manner, providing a frequency resolution that can be varied and made as high as required, which is a desirable property for processing oscillatory signals lacked by previously presented DSTs. Thanks to its flexible sampling scheme, the behavior of the developed transform in the TF domain can be adjusted easily; with specific parameter settings, for example, it samples the TF domain dyadically, while by choosing different settings, it may act as a STFT. The spectral partitioning is performed through asymmetric raised-cosine windows whose collective amplitude is unitary over the signal spectrum to ensure that the transform is easily and exactly invertible. The proposed DST retains all the appealing properties of the original ST, representing a local image of the Fourier transform; it requires low computational complexity and returns a modest number of TF coefficients. To confirm its effectiveness, the developed transform is utilized for different applications using real-world and synthetic signals.     

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios.