凸Cauchy Schwarz独立分量分析盲声源分离
Convex Cauchy Schwarz Independent Component Analysis for Blind Source Separation
摘要：
提出了一种新的用于独立分量分析（ICA）和盲声源分离（BSS）的高性能凸柯西-施瓦茨发散度（CCS-DIV）测度。CCS DIV测度是通过将凸函数集成到Cauchy Schwarz不等式中来开发的。通过引入凸性质量参数，该测度具有较宽的凸性曲率控制范围。在此基础上，构造了一种新的CCS ICA算法，并结合基于Parzen窗分布的非参数形式。此外，成对迭代格式被用来解决BSS中的高维问题。本文提出了两种两两非参数ICA算法，一种是基于梯度下降法，另一种是基于Jacobi迭代法。几个案例研究方案进行无噪声和嘈杂的混合语音和音乐信号。最后，通过与FastICA、RobustICA、Convex ICA（C ICA）等现有算法的度量比较，验证了CCS ICA算法的优越性.
英文摘要：
We present a new high performance Convex Cauchy Schwarz Divergence (CCS DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS DIV measure is developed by integrating convex functions into the Cauchy Schwarz inequality. By including a convexity quality parameter, the measure has a broad control range of its convexity curvature. With this measure, a new CCS ICA algorithm is structured and a non parametric form is developed incorporating the Parzen window based distribution. Furthermore, pairwise iterative schemes are employed to tackle the high dimensional problem in BSS. We present two schemes of pairwise non parametric ICA algorithms, one is based on gradient decent and the second on the Jacobi Iterative method. Several case study scenarios are carried out on noise free and noisy mixtures of speech and music signals. Finally, the superiority of the proposed CCS ICA algorithm is demonstrated in metric comparison performance with FastICA, RobustICA, convex ICA (C ICA), and other leading existing algorithms.
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