A geometric approach to blind separation of nonnegative and dependent source signals

作者：Naanaa Wady; Nuzillard Jean Marc^*

来源：Signal Processing, 2012, 92(11): 2775-2784.

DOI：10.1016/j.sigpro.2012.05.019

• Blind source separation
• Dependent signals
• Convex geometry
• Facet identification
• Dual cone

Blind source separation (BSS) consists in processing a set of observed mixed signals to separate them into a set of original components. Most of the current blind separation methods assumes that the source signals are %26quot;as statistically independent as possible%26quot; given the observed data. In many real-world situations, however, this hypothesis does not hold. In order to cope with such signals, a first geometric method was proposed that separates statistically dependent signals, provided that they are nonnegative and locally orthogonal. %26lt;br%26gt;This paper presents a new geometric method for the separation of nonnegative source signals which relies on a working assumption that is weaker than local orthogonality. The separation problem is expressed as the identification of relevant facets of the data cone. After a rigorous proof of the proposed method, the details of the separation algorithm are given. Experiments on signals from various origins clearly show the efficiency of the new procedure.

• 出版日期2012-11