ISBN 9783843950930

978-3-8439-5093-0, Reihe Ingenieurwissenschaften

Andreas Brendel
From Blind to Semi-Blind Acoustic Source Separation based on Independent Component Analysis

300 Seiten, Dissertation Universität Erlangen-Nürnberg (2022), Softcover, A5

Zusammenfassung / Abstract

Typical acoustic scenes consist of multiple superimposed sources, where some of them represent desired signals, but often many of them are undesired sources, e.g., interferers or noise. Hence, source separation and extraction, i.e., the estimation of the desired source signals based on observed mixtures, is one of the central problems in audio signal processing. A promising class of approaches to address such problems is based on Independent Component Analysis (ICA), an unsupervised machine learning technique. These methods enjoyed a lot of attention from the research community due to the small number of assumptions that have to be made about the considered problem. In this thesis, the problem of acoustic source separation and extraction is treated by Convolutive Blind Source Separation (CBSS) approaches based on ICA. As a basis for the thesis, we show and investigate relations between two well-known CBSS algorithms, IVA and TRINICON theoretically and experimentally. A crucial aspect of CBSS is the development of optimization schemes that allow for fast and computationally efficient iterative minimization of the CBSS cost function. In the second part of the thesis, we focus on optimization approaches based on the majorize-minimize principle, analyze state-of-the-art methods and propose a new optimization approach originating from a negentropy perspective. The last part of the thesis is dedicated to the derivation of a framework for semi-blind source separation, i.e., source separation that supports CBSS methods with prior knowledge, from a maximum a posteriori perspective. We demonstrate the use of this framework by incorporating spatial prior knowledge that enables a solution to the outer permutation ambiguity and allows to even address underdetermined problems. Finally, the integration of a background model allows to deal with overdetermined situations and yields computationally efficient update schemes.