基于时域去相关预处理的卷积混合语音盲分离算法

因语音信号自身的相关性及不平稳性,使卷积混合语音信号的盲分离变得十分困难.本文提出了一种基于时域去相关预处理的卷积混合语音盲分离时域算法.该算法采用自适应格型预测误差滤波器对语音信号进行时域去相关处理,同时基于空域去相关算法完成卷积混合语音分离.该算法充分考虑了语音信号自身的相关性及不平稳性,具有计算量小、收敛速度快、稳定性好的优点.仿真实验验证了该算法在对卷积混合语音信号进行盲分离时的有效性.     

初始分离矩阵优化的在线盲源分离算法

针对在线盲源分离算法收敛速度受初始分离矩阵影响的问题,提出一种基于人工蜂群算法(ABC)的初始分离矩阵优化的在线盲源分离算法。该算法利用人工蜂群算法较强的搜索能力,在盲源分离的初始阶段以批处理的方式进行分离矩阵的寻优,使得算法获得较好的初始迭代点,然后采用梯度下降法以在线的方式实现分离,从而提高算法的整体收敛性能。仿真结果证明了所提算法的有效性,并且其适用于混合矩阵时变的情形。     

采用非负矩阵分解的语音盲分离

针对多通路语音信号的欠定卷积混合模型,提出一种基于非负矩阵分解(NMF)的语音盲分离方法。该方法使用高斯分量对源信号的短时傅里叶变换(STFT)进行表示,高斯分量由基于板仓-斋藤(Itakura-Saito(IS))散度的非负矩阵分解的因子所组成。使用极大期望值算法(EM)求解参数,并对信号进行重组。该方法被应用到双声道立体声信号的盲分离实验,实验结果表明了该方法的有效性。     

基于Givens矩阵和联合非线性不相关的盲源分离新算法

针对源信号统计独立的盲源分离(Blind Source Separation,BSS)问题,提出了一种基于Givens矩阵和联合非线性不相关的盲源分离新算法.由于分离信号独立性的度量是影响算法有效性的重要因素,因此首先提出了一种改进的度量独立性的方法,该方法以独立源信号的联合非线性不相关来度量独立性;其次,结合Givens矩阵可以对分离矩阵施加正交性约束且能减少要估计参数个数的性质,将盲源分离问题转化成无约束优化问题,并利用拟牛顿法中的BFGS算法求解该无约束优化问题,得到分离矩阵;最后,通过模拟混合信号和真实语音混合信号的分离实验验证了该算法的有效性.     

基于改进DBSCAN算法估计欠定混合矩阵的应用研究

针对欠定盲源分离（Underdetermined blind source separation, UBSS）问题,采用基于密度的空间聚类（Density based spatial clustering of applications with noise, DBSCAN）算法估计聚类中心时易陷入局部最优,因此由聚类中心坐标构成的混合矩阵的精度降低,导致信号分离结果不理想。本文在DBSCAN基础上提出布谷鸟自适应搜索群优化算法（Cuckoo adaptive search swarm optimization of density based spatial clustering of applications with noise, CASSO-DBSCAN）,该算法依据Levy飞行策略增强全局自适应搜索能力,并利用群体学习思想精细寻优得到最优解,从而更加精准地估计聚类中心。通过语音信号的盲源分离仿真实验对该算法进行验证,结果表明,该算法能够有效改善欠定混合矩阵的估计精度,具有良好的鲁棒性,证明了其可行性。     

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.     

一种基于时域解相关的双通道语音分离算法

提出了一种基于时域解相关的卷积混合语音盲分离时域算法。该算法利用相关系数对语音信号进行时域去相关处理,同时基于二阶统计量完成双通道语音分离。该算法充分考虑了语音信号本身的相关性及不平稳性,收敛速度快,稳定性好,为减小计算量提出了该算法的递推改进算法。仿真实验表明,在对卷积混合双通道语音信号进行盲分离时,该算法是非常有效的。     

PID控制原理在语音信号盲源分离中的应用

语音信号在非平稳系统中是动态混合的,为了实时抑制盲源分离过程中的非平稳混合扰动,加快收敛速度,减小稳态误差,提出了一种应用PID控制原理的自适应盲源分离算法。依据一种无预处理的自适应盲源分离算法建立PID控制模型,调节学习速率,跟踪语音信号的分离过程,实时减小由非平稳混合引入的分离误差,动态更新分离矩阵。在混合矩阵缓变和突变两种情形下分别对PID参数整定和语音信号的分离进行仿真分析,结合经典算法对比提出算法的性能。仿真与对比结果表明,提出的算法适用于非平稳混合系统语音信号的分离,算法性能较经典算法有改善。     

PCA与ICA相结合的语音信号盲分离

针对ICA用于语音信号盲分离时,由于数据量过大、迭代次数过多引起的收敛速度慢的问题,采用一种PCA和ICA相结合的盲分离算法PCA-ICA。通过PCA对混合语音信号进行白化处理,消除了原始各道数据间的二阶相关性。在仿真实验中,采用相似系数矩阵作为评价混合语音信号分离效果的标准,结果表明PCA-ICA算法与ICA算法相比,在达到几乎相同的相似系数矩阵的情况下,迭代次数减少了90%,从而分离速度提高了3倍,有效地解决了ICA分离算法收敛速度慢的问题。     

基于改进的萤火虫优化算法的混合语音盲分离

针对传统盲源分离优化算法对分离性能影响较大的局限性,提出了一种基于改进的萤火虫优化的混合语音盲分离算法。将萤火虫的飞行跨度由固定取值变为由新构造的函数自适应调整,在加快收敛速度的同时避免算法早熟现象的发生。实验结果表明,与基于自然梯度、标准萤火虫和粒子群优化的盲分离算法相比,新算法对混合语音信号的分离效果较好,在收敛速度和分离能力方面都有所提升。     

Mixing Audiovisual Speech Processing and Blind Source Separation for the Extraction of Speech Signals From Convolutive Mixtures

Looking at the speaker's face can be useful to better hear a speech signal in noisy environment and extract it from competing sources before identification. This suggests that the visual signals of speech (movements of visible articulators) could be used in speech enhancement or extraction systems. In this paper, we present a novel algorithm plugging audiovisual coherence of speech signals, estimated by statistical tools, on audio blind source separation (BSS) techniques. This algorithm is applied to the difficult and realistic case of convolutive mixtures. The algorithm mainly works in the frequency (transform) domain, where the convolutive mixture becomes an additive mixture for each frequency channel. Frequency by frequency separation is made by an audio BSS algorithm. The audio and visual informations are modeled by a newly proposed statistical model. This model is then used to solve the standard source permutation and scale factor ambiguities encountered for each frequency after the audio blind separation stage. The proposed method is shown to be efficient in the case of 2 times 2 convolutive mixtures and offers promising perspectives for extracting a particular speech source of interest from complex mixtures     

独立分量分析与时频掩蔽结合的语音盲分离

针对语音信号的欠定卷积混合模型,提出一种基于快速独立分量分析和自适应非线性二元时频掩蔽的语音盲分离方法。对输入的混合语音信号进行快速独立分量分析,将结果进行自适应非线性二元时频掩蔽;重复进行这两步处理,直到分离出所有的语音源信号。将分离出的语音源信号,再通过二元时频掩蔽合并可提高输出的质量,分离出的语音信号仍然能保留双声道立体声的效果。实验表明,该方法的性能大大优于DUET方法和BLUES方法,信噪比增益大幅提高。     

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Givens变换和二阶振荡W-C-PSO优化的盲源分离算法

针对智能算法在实现盲源分离时容易陷入局部最优且收敛速度缓慢的问题,提出一种基于Givens变换和二阶振荡粒子群优化的盲源分离算法。该算法首先将惯性权重与学习因子两个参数构造函数关系,使之共同调节算法迭代来提高算法的整体性与全局搜索能力;再引入二阶振荡环节增加种群的多样性,这样算法不易陷入局部最优;此外,采用Givens变换将分离矩阵转换成旋转角度表示形式来降低算法的复杂度。仿真表明,该算法能有效实现机械振动信号和语音信号的盲分离,并且相比其他算法具有更快的收敛速度和更好的分离性能。     

基于稳健联合分块对角化的卷积盲分离

针对卷积盲分离问题,提出一种新的矩阵联合分块对角化(Joint block diagonalization, JBD)算法. 现有的迭代非正交联合分块对角化算法都存在不收敛的情况,本文利用分离矩阵的特殊结构确保其可逆性,使得算法的迭代过程稳定. 在已知矩阵分块结构的条件下,首先,将卷积盲分离模型写成瞬时形式,并说明其满足联合分块对角化结构; 然后,提出联合分块对角化的代价函数,依据代价函数的最小化等价于矩阵中每个分块的范数最小化, 将整个分离矩阵的迭代更新转化成每个分块的迭代更新;最后,利用最小化条件得到迭代算法. 实数和复数两种情况下的算法都进行了推导.基本实验验证了新算法在不同条件下的性能; 仿真实验中对在时域和频域都重叠的信号的卷积混合进行盲分离,实验结果验证了新算法具有更好的分离性能和更稳定的分离能力.     

On the Assumption of Spherical Symmetry and Sparseness for the Frequency-Domain Speech Model

A new independent component analysis (ICA) formulation called independent vector analysis (IVA) was proposed in order to solve the permutation problem in convolutive blind source separation (BSS). Instead of running ICA in each frequency bin separately and correcting the disorder with an additional algorithmic scheme afterwards, IVA exploited the dependency among the frequency components of a source and dealt with them as a multivariate source by modeling it with sparse and spherically, or radially, symmetric joint probability density functions (pdfs). In this paper, we compare the speech separation performances of IVA by using a group of l^p-norm-invariant sparse pdfs where the value of and the sparseness can be controlled. Also, we derive an IVA algorithm from a nonparametric perspective with the constraint of spherical symmetry and high dimensionality. Simulation results confirm the efficiency of assuming sparseness and spherical symmetry for the speech model in the frequency domain.     

Blind Source Separation of Postnonlinear Convolutive Mixture

In this paper, a novel solution is developed to solve blind source separation of postnonlinear convolutive mixtures. The proposed model extends the conventional linear instantaneous mixture model to include both convolutive mixing and postnonlinear distortion. The maximum-likelihood (ML) approach solution based on the expectation-maximization (EM) algorithm is developed to estimate the source signals and the parameters in the proposed nonlinear model. In the proposed solution, the sufficient statistics associated with the source signals are estimated in the E-step, while the model parameters are optimized through these statistics in the M-step. However, the complication resulted from the postnonlinear function associated with the mixture renders these statistics difficult to be formulated in a closed form and hence causes intractability in the parameter optimization. A computationally efficient algorithm is proposed which uses the extended Kalman smoother (EKS) to facilitate the E-step tractable and a set of self-updated polynomials is used as the nonlinearity estimator to facilitate closed form estimations of the parameters in the M-step. The theoretical foundation of the proposed solution has been rigorously developed and discussed in details. Both simulations and recorded speech signals have been carried out to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithms.     

Enhancement of speech signals separated from their convolutive mixture by FDICA algorithm

This paper presents a novel method for the enhancement of independent components of mixed speech signal segregated by the frequency domain independent component analysis (FDICA) algorithm. The enhancement algorithm proposed here is based on maximum a posteriori (MAP) estimation of the speech spectral components using generalized Gaussian distribution (GGD) function as the statistical model for the time–frequency series of speech (TFSS) signal. The proposed MAP estimator has been used and evaluated as the post-processing stage for the separation of convolutive mixture of speech signals by the fixed-point FDICA algorithm. It has been found that the combination of separation algorithm with the proposed enhancement algorithm provides better separation performance under both the reverberant and non-reverberant conditions.     

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。