Sigma-delta resolution enhancement for far-field acoustic source separation

Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density microphone arrays where distance between sensor elements is much smaller than the wavelength of the signal of interest. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficient to overcome the artifacts due to cross-channel redundancy, non-homogenous mixing and high-dimensionality of the signal space. In this paper we propose a novel framework that overcomes these limitations by integrating learning algorithms directly with analog-to-digital conversion. At the core of the proposed approach is a novel regularized min-max optimization approach that yields "delta-sigma" limit-cycles. An on-line adaptation modulates the limit-cycles to enhance resolution in the signal sub-spaces containing non-redundant information. Numerical experiments simulating far-field recording conditions demonstrate consistent improvements over a benchmark setup used for independent component analysis (ICA).