Directionally constrained filterbank ICA

Abstract

A modification is proposed to the independent component analysis (ICA)-based filterbank approach in consideration to its structural similarity with binaural auditory model of sound source localization. The estimated sound locations provide an additional cue to the learning algorithm, which is utilized for initialization and imposition of directional constraints on the subband separation networks. Directionally constrained filterbank ICA (DC-FBICA) gives faster convergence and improves separation performance for noisy mixtures having significant spectral overlap among the convolved mixture and the corrupting noise. However, only slight improvement in separation performance is observed when the additive noise is a low frequency noise, although faster convergence is still observed.