Independent Vector Analysis based Convolutive Speech Separation by Estimating Entropy using Recursive Copula Splitting

Speeches in real world environment are normally mixed together convolutedly. The speech mixtures are not instantaneous at all. Rather speeches are mixed component wise. Independent Vector Analysis (IVA) is an approach for separating convolutive mixture in frequency domain. Entropy estimation is an important part of IVA. In this paper, IVA is implemented by estimating entropy using recursive copula splitting. It measures entropy by decomposing probability density function (PDF) into a product of marginal (1D) densities and a copula. This entropy estimator improves IVA performance in different types of real world speech mixtures. We have proved that by estimating entropy using recursive copula splitting makes IVA algorithm simple and more efficient'.