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.     

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。     

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.     

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...     

频域盲源分离的邻频幅角比排序算法

频域盲源分离方法通过STFT变换将时域的线性卷积模型转化为频域的瞬时混合模型,可以利用瞬时混合的成熟算法,然而缺点是存在排列和尺度的不确定性,会使逆STFT变换后的信号发生再次混叠。对分离矩阵内部结构进行研究,提出邻频幅角比的概念,通过纠正图样中发生排列错误频点处的分离矩阵结构,达到正确拟合已分离信号的目的。仿真结果表明,邻频幅角比排序算法可以纠正大多数频点上的排序错误,正确进行盲源分离。     

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。     

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.     

基于小波域的非平稳卷积混合 语音信号的自适应盲分离

一些卷积混合信号的盲分离算法是迭代性的，不适于实时应用．为此提出一种基于小波域的算法，用于卷积混合信号的自适应盲分离．对基于小波域的算法进行仿真，并与频域盲信号分离算法进行对比，结果表明所提出的算法能提高盲信号分离的性能。     

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。     

An Approach for Solving the Permutation Problem of Convolutive Blind Source Separation Based on Statistical Signal Models

In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. Transformed to the frequency domain, existing algorithms can efficiently solve the reduction of the source separation problem into independent instantaneous separation in each frequency bin. However, this independency leads to the problem of correctly aligning these single bins. The new algorithm models the frequency-domain separated signals by means of the generalized Gaussian distribution and employs the small deviation of the parameters between neighboring bins for the detection of correct permutations. The performance of the algorithm will be demonstrated on synthetic and real-world data.     

Underdetermined Convolutive Blind Source Separation via Time–Frequency Masking

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k -means or fuzzy c -means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.     

Resolving permutation ambiguity in correlation-based blind image separation

We address the problem of permutation ambiguity in blind separation of multiple mixtures of multiple images (resulting, for instance, from multiple reflections through a thick grass plate or through two overlapping glass plates) with unknown mixing coefficients. In this paper, first we devise a generalized multiple correlation measure between one gray image and a set of multiple gray images and derive a decorrelation-based blind image separation algorithm. However, many blind image separation methods, including this algorithm, suffer from a permutation ambiguity problem that the success of the separation depends upon the selection of permutations corresponding to the orders of the update operations. To solve the problem, we improve the first algorithm above by decorrelating the mixtures while searching for the appropriate update permutation using a pruning technique. We show its effectiveness through experiments with artificially mixed images and real images.     

面向置换检验的冗余对比模式过滤算法

置换检验方法在进行对比模式挖掘时,返回结果中存在许多冗余对比模式。利用Charm方法挖掘样本集合中的对比模式,提出基于固定属性置换的FSPRP和FEPRP算法,依次为不同长度的对比模式构建零分布,从而过滤冗余对比模式。FSPRP算法通过生成一定数量的置换样本集合构建零分布,FEPRP算法则通过计算每个模式的对比性度量值分布合并建立零分布。实验结果表明,FSPRP和FEPRP算法相较于比较约束法能够过滤较多数量的冗余对比模式,并且FEPRP算法生成的零分布更接近精确零分布。     

Improvement in monaural speech separation using sparse non-negative tucker decomposition

A monaural speech separation/enhancement technique based on non-negative tucker decomposition (NTD) has been introduced in this paper. In the proposed work, the effect of sparsity regularization factor on the separation of mixed signal is included in the generalized cost function of NTD. By using the proposed algorithm, the vector components of both target and mixed signal can be exploited and used for the separation of any monaural mixture. Experiment was done on the monaural data generated by mixing the speech signals from two speakers and, by mixing noise and speech signals using TIMIT and noisex-92 dataset. The separation results are compared with the other existing algorithms in terms of correlation of separated signal with the original signal, signal to distortion ratio, perceptual evaluation of speech quality and short-time objective intelligibility. Further, to get more conclusive information about separation ability, speech recognition using Kaldi toolkit was also performed. The recognition results are compared in terms of word error rate (WER) using the MFCC based features. Results show the average improved WER using proposed algorithm over the nearest performing algorithm is up to 2.7% for mixed speech of two speakers and 1.52% for noisy speech input.     

Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling

Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures; thus, sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources, and singing voices, show that this algorithm outperforms the general nonnegative matrix factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side effect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which is a difficult task for general multipitch estimation (MPE) algorithms.     

一种并行主偏度分析算法及其在盲源分离上的应用

盲源分离是一种从混合信号中提取和恢复源信号的信号处理方法。在众多盲源分离算法中,主偏度分析算法是近年来出现的一种以三阶统计量为目标函数的盲源分离算法,其运算速度快于常规的盲源分离算法,但因其采用了串行的计算方式,在使用中存在误差累积问题。针对这一问题,本文在主偏度分析算法基础上进行改进,提出了一种并行主偏度分析算法。该算法以并行计算代替串行计算,可以同时估计出各个独立成分对应的方向,避免了误差累积问题。数值仿真实验表明,与主偏度分析算法相比,并行主偏度分析算法既保持了计算速度,同时提高了对源信号的估计准确性。     

基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法

基于矩阵置换的分块压缩感知（BCS）引入矩阵置换的策略,使复杂子块和稀疏子块向介于两者中间的稀疏度水平变化,用单一采样率采样时可以减少块效应,但仍存在块间稀疏度均衡效果较差的问题。为了得到更好的重构效果,提出基于波浪式矩阵置换的稀疏度均衡BCS（BCS-RMP）算法。首先,在采样前对图像进行矩阵置换的预处理,通过波浪式置换矩阵对图像各子块的稀疏度进行均衡;然后,采用相同的测量矩阵对子块进行采样,在解码侧进行重构;最后,通过波浪式置换逆矩阵对重构结果进行逆变换得到最终的重构图像。仿真结果表明,与现有矩阵置换算法相比,当选择合适的子块大小和采样率时,所提波浪式矩阵置换算法可有效提高图像的重构质量,且能更准确地体现细节信息。