Sigma-delta learning for super-resolution source separation on high-density microphone arrays

The performance of acoustic source separation algorithms significantly degrades when they applied to signals recorded using miniature microphone arrays where the distances between the microphone elements are much smaller than the wavelength of acoustic signals. 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 large cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the signal space. This paper presents some of the recent progress in the area of sigma-delta learning which integrates statistical learning with analog-to-digital process and enables super-resolution auditory localization and separation. Experiments with synthetic and real recordings demonstrate significant and consistent performance improvements when the proposed approach is used as the analog-to-digital front-end to conventional source separation algorithms.