  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.
