2020/03/20 by Andreas Brendel, Brendel, Andreas, Walter Kellermann +1 · 1 citation
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.09531
openalex publication_date 2020/03/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.