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

卷积混合环境下盲源分离实时信号处理和独立分量分析实时信号处理研究

• 批准号: 16500134
• 负责人: DING Shuxue
• 金额: $ 1.98万
• 依托单位: The University of Aizu
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2004
• 资助国家: 日本
• 起止时间: 2004 至 2006
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16500134/
• 关键词: Blind Source Separation; Independent Component Analysis; Convolutive Mixture; Blind Acoustic Source Separation; 実時間信号処理; 実環境; 実時間処理

(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

(1)提出了一种基于同时摄动随机逼近梯度学习的独立分量分析（ICA）新算法。该算法在动态混合环境下能很好地工作。即使对于非平稳和/或非等独立分布（non-I.I.D）信号，该算法也能快速收敛，因此该算法非常适合于大多数实时应用。(2)提出了一种在真实室内环境中对多个扬声器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为时频域。然后，我们基于ICA算法分离每个频率仓中的源。我们选择复杂版本的不动点迭代（CFPI）作为算法。(3)提出了一种卷积盲源分离（CBSS）实时信号处理算法。我们采用了重叠保存策略，并考虑了在频域内分离源的问题。我们引入了一个改进的相关矩阵，并通过矩阵的对角化进行了CBSS。我们提出了一种方法，可以通过求解所谓的CBSS正态方程来对角化修改后的相关矩阵。实时分离的卷积混合源可以执行。(4)解决了利用源信号频域稀疏性的CBSS问题。提出了一种基于自然梯度的复稀疏表示方法。在此基础上，进一步提出了一种基于复杂稀疏表示的CBSS方法。所开发的CBSS算法工作在频域。(5)提出了一种求解ICA问题的新型算法。该算法基于一种有效的更新方案，其中学习更新作为一系列标准正交矩阵变换。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含任何预定参数，比如学习步长。少

项目成果

A Scalable Mobile Phone-Based System for Multiple Vital Signs Monitoring and Healthcare

用于多种生命体征监测和医疗保健的可扩展的基于移动电话的系统

• 发表时间: 2005
• 期刊: Journal of Pervasive Computing and Communications 1-2
• 作者: Wenxi Chen;Daming Wei;Shuxue Ding;Michael Cohen;Hui Wang;Shigeru Tokinoya;Naotoshi Takeda
• 通讯作者: Naotoshi Takeda

An algorithm for real-time independent component analysis in dynamic environments

• DOI: 10.1109/mwscas.2004.1354302
• 发表时间: 2004-07
• 期刊: The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04.
• 作者: Shuxue Ding;D. Wei;S. Omata

Real-Time Independent Component Analysis Based on Gradient Learning with Simultaneous Perturbation Stochastic Approximation

• DOI: 10.1007/978-3-540-30133-2_47
• 发表时间: 2004-09
• 作者: Shuxue Ding;Jie Huang;D. Wei;S. Omata

Independent Component Analysis without Predetermined Learning Parameter

无需预先确定学习参数的独立分量分析

• 发表时间: 2006
• 期刊: Proc. CIT 2006 (2006 IEEE International Conference on Computer and Information Technology) CIT 2006
• 作者: 川上愛;中村敏枝;河瀬諭ほか;丁 数学
• 通讯作者: 丁 数学

Signal extensions in independent component analysis and its application for real-time processing

• DOI: 10.1109/cit.2004.1357299
• 发表时间: 2004-09
• 期刊: The Fourth International Conference onComputer and Information Technology, 2004. CIT '04.
• 作者: Shuxue Ding;Jie Huang;D. Wei;S. Omata