A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis

Abstract

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.

References in corpus (4)

• Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
• Independent Vector Analysis: Identification Conditions and Performance Bounds
• A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis
• Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization