A multimodal approach for frequency domain independent component analysis with geometrically-based initialization
A multimodal approach for frequency domain independent component analysis with geometrically-based initialization
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发表时间：
2008-08
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通讯作者：
S. M. Naqvi;Y. Zhang;T. Tsalaile;S. Sanei;J. Chambers
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作者：
S. M. Naqvi;Y. Zhang;T. Tsalaile;S. Sanei;J. Chambers
提出了一种新的复值频域信号独立分量分析（伊卡）的多模态方法，该方法利用视频信息对说话人和麦克风进行几何描述。这种几何信息，视觉方面，被纳入到复杂的伊卡算法的初始化为每个频率箱，因此，该方法是多模态的，因为两个信号模态，语音和视频，被利用。分离结果表明，传统的频域卷积盲源分离（BSS）系统的显着改善。重要的是，固有的置换问题，在频域BSS（复值信号）的收敛速度的改善，静态源，被证明是解决在每个频率箱的水平的仿真结果。我们还强调，某些不动点算法提出的Hyva Ehrinen等。例如，或者它们的约束版本对于复值信号是无效的。
A novel multimodal approach for independent component analysis (ICA) of complex valued frequency domain signals is presented which utilizes video information to provide geometrical description of both the speakers and the microphones. This geometric information, the visual aspect, is incorporated into the initialization of the complex ICA algorithm for each frequency bin, as such, the method is multimodal since two signal modalities, speech and video, are exploited. The separation results show a significant improvement over traditional frequency domain convolutive blind source separation (BSS) systems. Importantly, the inherent permutation problem in the frequency domain BSS (complex valued signals) with the improvement in the rate of convergence, for static sources, is shown to be solved by simulation results at the level of each frequency bin. We also highlight that certain fixed point algorithms proposed by Hyvärinen et. al., or their constrained versions, are not valid for complex valued signals.