Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning

TL;DR

This paper introduces a semi-blind source separation method using sparse representations and online dictionary learning to effectively separate a known-structure source from an unknown background in single-channel audio data.

Contribution

It presents a novel online dictionary learning approach for semi-blind source separation that adapts to background sources using only the observed data.

Findings

Effective separation demonstrated in simulated audio tasks

Online dictionary learning adapts to background source structure

Applicable to various single-channel source separation problems

Abstract

This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on local sparse approximations along the lines of recent efforts in sparse representations and dictionary learning. A key feature of our procedure is the online learning of dictionaries (using only the data itself) to sparsely model the background source, which facilitates its separation from the partially-known source. Our approach is applicable to source separation problems in various application domains; here, we demonstrate the performance of our proposed approach via simulation on a stylized audio source separation task.