Sparse Component Analysis

In this paper, we investigate Sparse Component Analysis, an underdetermined blind source separation technique based on instantaneous mixing models. Unlike conventional automated algorithms such as ICA or SOBI, prior implementations lacked automation capabilities. Our enhanced algorithm introduces automated execution mechanisms while significantly improving computational efficiency and accuracy. The current methodology is specifically designed for vibration signal processing. For applications involving speech signals, users must adapt the mixing matrix estimation module in the codebase (a manageable modification), whereas the core ℓ1-norm minimization algorithm requires no alterations. Technical details are further elaborated in our companion paper: Identification of modal parameters using an improved sparse blind source separation. Note that publication timelines may extend beyond the current timeframe (end of 2014). While the fundamental algorithm principles pre-existed, practical implementations faced substantial limitations. Our proposed solution represents an original contribution, though certain limitations remain subject to ongoing refinement. We actively welcome feedback and suggestions, as constructive criticism drives iterative improvement. Contact information, including email details, is embedded within the code documentation for technical discussions.