Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Abstract

The problem of blind source separation (BSS) has been intensively studied by the signal processing community. The first solutions to deal with BSS were proposed in the 1980's and are founded on the concept of independent component analysis (ICA). More recently, aiming at tackling some limitations of ICAbased methods, much attention has been paid to alternative BSS approaches. In this tutorial, in addition to providing a brief review of the classical BSS framework, we present two research trends in this area, namely source separation over Galois fields and sparse component analysis. For both subjects, we provide an overview of the main criteria, highlighting scenarios that can benefit from these more recent BSS paradigms.

Genre Classification for Brazilian Music using Independent and Discriminant Features

“…The classifier training process may be hampered if redundant features are used. To avoid this problem, independent component analysis (ICA)[21] is proposed in this work as a preprocessing step.…”