Abstract
The blind source separation problem arises when one attempts to recover source signals from their linear mixtures without detailed knowledge of the mixing process. Solutions are nonunique and have degrees of freedom in scaling and permutation. One may impose equality (hard) constraints to fix these scaling parameters; however, small divisor problems may appear. In this paper, an iterative method is introduced based on information maximization and auxiliary equations for the scaling parameters. The method dynamically selects scaling parameters and avoids divisions, and it is called the soft-constrained method. Global boundedness of the algorithm is proved. The convergence of solutions in the large time and small step size regimes is analyzed. An upscaled, dynamically averaged equation for the separating matrix is derived. The stable and accurate separation performance is illustrated by examples of instantaneous random mixtures of two and eight sound signals.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
