Researches of Real-Time Signal Processing for Blind Source Separation in Convolutive Mixing Environment and Real-Time Signal Processing for Independent Component Analysis

(1)提出了一种基于同时扰动随机逼近(SPSA)的梯度学习的独立分量分析(ICA)新算法。该算法能够在动态混合环境下，在在线模式下工作。即使对于非平稳的和/或非同分布的独立分布(非I.I.D.)，它的收敛速度也非常快。因此，该算法非常适合于大多数实时应用。(2)提出了一种在真实房间环境中对多个说话人混合产生的声源进行盲分离的方法。我们首先将记录的信号转换到时频域。然后，我们基于ICA算法分离每个频段中的源。选择复数形式的定点迭代(CFPI)作为算法。(3)提出了一种卷积盲源分离的实时信号处理算法。我们应用了重叠保存策略，并考虑了fr…中的源分离问题。更多的等级域。我们引入了一种改进的相关矩阵，并通过对角化实现了CBSS。我们提出了一种方法，可以通过求解CBSS的一个所谓的法方程来对角化修正的相关矩阵。利用源信号在频域的稀疏性，实现了卷积混合信号的实时分离。提出了一种新的复数稀疏表示的自然梯度方法。在此基础上，进一步提出了一种基于复稀疏表示的CBSS方法。改进后的CBSS算法在频域内工作。(5)提出了一种新型的ICA算法。该算法基于一种有效的更新方案，其中学习更新就像一系列的正交化矩阵变换。该算法的一个吸引人的特点是它不像基于梯度的算法那样包括任何预定的参数，例如学习步长。较少

(1) We present a novel algorithm for independent component analysis (ICA) based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). This algorithm can work well in on-line mode, in a dynamic mixing environment. It converges very fast even for non-stationary, and/or non-identically independent distributed (non-I.I.D.) signals, so that the algorithm is very suitable for most real-time applications.(2) We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time-frequency domain. We then separate the sources in each frequency bin based on an ICA algorithm. We choose the complex version of fixed point iteration (CFPI) as the algorithm.(3) We proposed an algorithm for real-time signal processing of convolutive blind source separation (CBSS). We applied an overlap-and-save strategy, and considered the issue of separating sources in the fr … More equency domain. We introduced a modified correlation matrix and performed CBSS by diagonalization of the matrix. We proposed a method that could diagonalize the modified correlation matrix by solving a so-called normal equation for CBSS. A real-time separation of the convolutive mixtures of sources can be performed.(4) CBSS that exploits the sparsity of source signals in the frequency domain was addressed. We proposed a novel natural gradient method for complex sparse representation. Moreover, a new CBSS method was further developed based on complex sparse representation. The developed CBSS algorithm works in the frequency domain.(5) This research presents a new type of algorithm for solving ICA problems. This new algorithm was based on an effective updating scheme in which learning updating acts as a series of orthonormal matrix transformations. One attractive feature of the algorithm is that it does not include any predetermined parameters, such as a learning step size, as do gradient-based algorithms. Less