Separating Convolutive Mixtures with Trinicon

Blind source separation (BSS) algorithms are often categorized as either narrowband or broadband algorithms depending on whether their respective cost functions aim at individual DFT bins or the entire broadband signal. In this contribution, we present comparable general natural gradient-based formulations of both concepts based on the TRINICON framework. As a distinctive feature, narrowband algorithms imply an internal permutation and scaling problem. We show that the common DOA estimation-based methods for aligning the permutations effectively rely on geometric a-priori knowledge, and we explain why they need to be complemented by additional repair mechanisms for robust BSS. The latter can already be viewed as approximations of the generic TRINICON broadband algorithm. As a conclusion, we propose to always use a generic broadband algorithm as a starting point for the design of new BSS algorithms