Audio source separation from the mixture using empirical mode decomposition with independent subspace analysis

Abstract

In this paper we decompose the Hilbert Spectrum of an audio mixture into a number of subspaces to segregate the sources. Empirical mode decomposition (EMD) together with Hilbert transform produces Hilbert spectrum (HS), which is a fineresolution time-frequency representation of a non-stationary signal. EMD decomposes the mixture signal into some intrinsic oscillatory modes called intrinsic mode function (IMF). HS is constructed from the instantaneous frequency responses of IMFs. Some frequency independent basis vectors are derived using independent component analysis (ICA). Kulback-Laibler divergence based k-means clustering algorithm is proposed to group the basis vectors into number of desired sources. Then projecting HS on to the grouped basis vectors derives the independent source subspaces. The time domain source signals are assembled by applying some post processing on the subspaces. We have also produced some experimental results using our proposed separation algorithm.