Independent Vector Analysis for Real World Speech Processing

摘要

We introduce independent vector analysis (IVA) which is an extension of independent component analysis (ICA) to multivariate components. In a set of ICA mixtures, IVA groups dependent source components across different ICA mixtures and regard them as a multivariate source. This new formulation is an efficient framework for solving the permutation problem in frequency-domain blind source separation (BSS) and its application to n × n speech separation problem has been very successful. In this paper, we present a short tutorial on IVA and summarize the various models that have been proposed to model the frequency components of speech.