基于邻近频点相关特性的水声信号盲源分离

排序和幅度不一致性是信号频域盲源分离的主要困难。该文建立了邻近频点相关特性理论,并针对水声信号进行深入研究,结论表明单个水声信号邻近频点间相关特性良好,且性能非常稳定;而两个不同水声信号邻近频点相关性非常弱。提出基于邻近频点相关特性的盲源分离算法,用于消除卷积信号盲源分离过程中排序不确定性,实验表明该方法对卷积混合形式的水声信号能取得较好分离效果。     

基于相邻频点幅度相关的语音信号盲源分离

排序和幅度不一致性是在频域进行信号盲源分离的主要困难。针对语音信号邻近频点间信号幅度相关性能良好这一特点，本文提出基于相邻频点间幅度相天的盲源分离算法，用以消除卷积信号盲源分离过程中排序不确定性。本算法理论简单，稳健性好。仿真结果表明该方法对卷积混合后的语音信号能得到较好的分离效果，并且耗时较短。     

基于影响因子的频域盲源分离排序算法

卷积混合信号盲源分离可以在频域得到有效解决,但频域盲源分离必须解决排序问题.本文研究了频点距离和各频点分离质量对基于相邻频点幅度相关性的排序算法的影响,提出了改进的频域盲源分离排序算法.改进算法通过影响因子来控制频点距离和各频点分离质量对排序的影响,距离小且分离质量好的频点设置较大影响因子,距离大或分离质量不好的频点则设置较小影响因子.文中详细讨论了影响因子的设定函数.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.实验结果表明了本文算法的有效性.     

基于区域增长校正的频域盲源分离排序算法

卷积盲源分离可以在频域得到有效解决,但频域盲源分离必须解决排序模糊问题。该文提出一种基于区域增长校正的频域盲源分离排序算法。首先对卷积混合信号短时傅里叶变换,在频域的各个频点处建立瞬时模型进行独立分量分析,在此基础上使用分离信号功率比的相关性,对所有频点进行逐点排序置换。其次根据阈值将排序后的结果划分为若干个小区域。最后按区域增长方式进行区域置换与合并,最终得到正确的分离信号。区域增长校正可最大限度地减少频点排序错误扩散现象,从而改善分离效果。在模拟和真实环境中分别进行语音盲源分离实验,结果表明所提算法的有效性。     

宽带恒定束宽盲波束形成算法

针对传统盲源分离算法对宽带阵列信号适用性较差的问题,提出一种基于时频分析的宽带恒定束宽盲波束形成算法。该算法首先将接收信号变换到时频域上并提取出单源点。然后,对单源点聚类并求解信号在不同频点上的导向矢量。最后,通过提出一种信号来向未知的空间响应变化约束方法,实现宽带恒定束宽盲波束形成。该算法避免了将宽带盲波束形成转换为卷积混合的盲源分离,因而不存在时域盲源分离算法中系统参数随滤波器阶数急剧增加的问题,也不存在频域算法中排序和幅度模糊的问题。仿真结果表明,算法能够较好地实现宽带信号的盲分离,且输出信干噪比高于时域、频域以及时频域盲源分离算法,实测数据的处理结果验证了该算法的实用性。      

基于相关矩阵的频域盲分离排序方法

排列模糊性和幅度模糊性一直是在频域上分离卷积混合信号所面临的主要问题,针对该问题,给出了一种基于相邻频点的幅度相关之和的、快速有效地解决频域分离算法中排列模糊性的方法;即通过定义相邻频点的相关矩阵并通过其置换形式来解决排列模糊问题,从而在频域上有效的分离源信号,仿真证明,该算法可以对卷积混合信号实行快速有效分离,分离效果理想,并大幅减少计算量。     

盲解卷积与窄带阵列模型结合的信号分离算法

在水声制导技术中,提出了一种高效分离算法,实现了对多目标源信号的分离,为系统后端对多目标定位提供了技术支持。窄带信号条件下,把盲分离与阵列信号处理结合起来．借助阵列模型把接收的混合信号变成解析信号,然后利用瞬时复值盲分离算法进行分离获得源信号的解析信号,取实部后便是实源信号。从而将实数的卷积混合转化为复数的瞬时混合,在盲分离阵列模型的基础上,通过复数盲分离的手段完成盲解卷积。解卷积恢复的多目标源信号十分有利于多目标特征识别与定位。通过构建正弦信号盲解卷积仿真实验,对文中提出的盲解卷积方法进行了验证。结果表明了该方法的正确性。     

利用时频域稀疏性的LFM信号盲源分离方法

信号分离是雷达电子对抗的重要环节。考虑到雷达信号在时频域具有稀疏性的特点,在独立分量分析的基础上,提出了一种基于时频域稀疏性的线性调频雷达信号盲源分离方法。首先对混合信号进行短时傅里叶变换,在每个频点利用自然梯度算法分离信号,由分离信号幅度的比值作为对源信号后验概率的估计;然后根据相邻频点后验概率序列的相关性进行排序,确保各个频点的分离信号属于同一个源信号;最后设计时频掩码分离信号。进行了线性调频雷达信号卷积混合的盲分离实验,所提方法分离结果明显优于传统独立分量分析方法的分离结果,验证了该方法的有效性。     

基于子空间分解的多通道盲解卷积算法

针对卷积混合信号,提出了一种新的多通道盲解卷积算法,该算法首先利用子空间分解方法,将信号卷积混合模型变换成线性混合模型,然后利用线性混合盲分离算法分离出源信号.该算法相对频域盲解卷积算法来说无需解决线性混合盲分离中存在的幅度和排列顺序的模糊性问题,而且该算法不要求信号独立同分布,只要求各源信号统计独立即可.因此,该算法可以直接在中频对观察信号进行处理.计算机仿真结果表明,该算法不仅能对不同频不同调制方式的通信信号进行盲解卷积,而且对同频同调制的通信信号,该算法同样有效.     

一种基于非线性PCA的卷积混合盲源分离算法

 本文针对卷积混合的盲源分离,首先提出了一种基于非线性主成分分析(PCA)的准则,然后推导出了一种递推最小二乘(RLS)算法.该算法为时域盲源分离算法,分为两个阶段,第一阶段为混合信号的白化,第二阶段为基于白化信号的RLS迭代.新算法不存在频域算法在每个频点的幅度与次序不确定性的问题,具有较快的收敛速度.文中最后通过仿真试验验证了本文算法的有效性.     

一种宽带信号波达方向估计的改进RSS算法

针对传统Rss (Rotate Signal Sub-space)算法的计算复杂度高,分辨力低等缺点,文中提出了一种基于宽带相干信号波达方向估计的改进RSS算法.该方法首先对接收数据进行共轭重构,再利用相干信号子空间法将带宽内各个频率点的信号子空间聚焦到参考频点下的同一信号子空间,然后利用均匀线阵建立数学模型,进行波达方向的估计.仿真实验结果验证了方法的有效性.     

A Novel and Fast Algorithm for Solving Permutation in Convolutive BSS,Based on Real and Imaginary Decomposition

In this paper, a new fast method for solving the permutation problem in convolutive BSS is presented. Typically, by transferring signals to the frequency domain, the convolutive BSS problem is converted to an instantaneous BSS, and deconvolution takes place in each frequency bin. However, another major problem arises which is permutation ambiguity in the frequency domain. Solving the permutation ambiguity for N sources in frequency domain needs N! comparisons between adjacent frequency bins. This drastically increases the overall computational complexity of the convolutive BSS. In our new approach, the complex-valued signals are decomposed into real and imaginary parts in each frequency bin. We show that the ideal mixing matrix has to possess a simple and symmetric structure. Accordingly, the structure can be exploited for solving the permutation ambiguity in frequency domain. Although separation in subband is accomplished by the FastICA algorithm, the proposed method requires modification of the separation algorithm, and a new structure is imposed on the mixing matrix. After that signals are separated by means of the FastICA, the permutation correction takes place only by N comparisons, decreasing the computational complexity. Comparing to five competitive methods, we experimentally demonstrate that permutation ambiguity is resolved accurately by this very fast approach while substantially decreasing the order of calculations. In terms of the separation performance and signal quality, the proposed method is superior to four of the compared methods and almost similar to the best of them.     

语音信号频域盲分离中一种频率对准方法

混合语音信号可以使用盲分离频域解法,对观测信号在每一个频点分别进行复值独立分量分析（CICA）算法来解混并得到分离信号,但带来了幅值和次序不定问题（后者又称频率对准）。讨论了频率对准算法中基于DOA估计的方法,并提出了一种基于分离矩阵初始化的频率对准方法,此方法易于实现。通过仿真表明,该方法较好地解决了次序不定问题．对卷积混合语音信号有较好的分离效果。     

一种时频域上的盲信号分离方法

滑动傅立叶变换是一种时频分析方法,本文详细推导了滑动傅立叶变换一个频率芯值与原信号的关系,提出了一个频率芯上信号盲分离算法,本算法实现容易,计算量少,计算机仿真结果证实所提出方法的正确性和有效性。     

Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation

In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.     

一种解决频域盲源分离模糊度问题的新方法

针对卷积混合信号的分离问题,提出一种解决频域分离算法中排列模糊性和幅度模糊性的改进方法,即通过ICA得到的一个频率点上的分离矩阵,作为计算下一个频率点分离矩阵的初始值来解决排列模糊性问题,再利用分离矩阵的逆变换来解决幅度模糊性,从而有效地分离出源信号.仿真证明,算法可以有效地分离出卷积混合信号,提升了平均信噪比,并大幅...     

基于性能权重聚类的频域盲分离排序算法

卷积混合盲源分离可以在频域得到有效解决，但频域盲分离必须要解决排序模糊性问题。本文提出了一种基于性能权重聚类的频域盲分离排序算法，该算法利用聚类来得到顺序参考，对各频点上分离信号的准确性进行计算，根据分离结果的准确性予以不同频点不同的聚类权重，从而提高聚类结果的可靠性。通过对频点进行分段处理可以有效抑制排序错误的传播，提高算法性能。最后通过多组仿真实验验证了基于性能权重聚类的频域盲分离排序算法的普适性与性能上的优越性，同时也探究了接收端个数对算法性能的影响。仿真结果表明本文提出的基于性能权重聚类的频域盲分离排序算法相较于传统的幅度相关性排序算法在信干比上会有2 dB左右的提升。接收天线数越多，算法分离性能越好。     

Acoustic echo-canceler using the FBAF algorithm

An acoustic echo-canceler for teleconferencing systems is realized based on the frequency bin adaptive filtering (FBAF) algorithm. In the FBAF algorithm, each frequency bin does an independent adaptive filtering, so that parallel processing can be used to increase the throughput of the system. Hardware size can be reduced to about 25% of the FIR time domain adaptive filter (TDAF) requirement. The realized echo canceler allows a comfortable conversation with only 8 ms of delay. The hardware prototype contains 12 VSP chips and one DSP chip, An ERLE (echo return loss enhancement) of 30 dB was achieved using this prototype hardware for an echo reverberation path with 260 ms delay. An efficient method for normalizing the convergence factor of the FBAF algorithm with a correlated input signal is given that speeds up the convergence rate. The performance is shown by computer simulation     

Localization of wideband signals using least-squares and totalleast-squares approaches

In this paper, we introduce a new focusing technique for localization of wideband signals. Relaxing the unitary assumption for the focusing matrices, we formulate the least-square (LS) and the total least-square (TLS) coherent signal-subspace methods. The TLS is an alternative to the conventional LS and uses the fact that errors can exist both in the focusing location matrix as well as in the estimated location matrix at a given frequency bin. To prevent the focusing loss, we use a class of focusing matrices that are constant under multiplication by their Hermitian transpose. The class of unitary matrices comports with this property. We then develop a new focusing technique based on a modification to the TLS (MTLS). It is shown that the computational complexity of the new technique is significantly lower than that for the rotational signal subspace method (RSS). The focusing gain of the new technique is also larger than the focusing gain of the RSS algorithm. The simulation study shows that, compared with the RSS, the new algorithm has a smaller resolution signal to-noise ratio (SNR)