Collaborative sources identification in mixed signals via hierarchical sparse modeling

A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on CHiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely model one of a set of possible classes. Then, the coding of the mixed signal is performed by efficiently solving a convex optimization problem that combines standard sparsity with group and collaborative sparsity. The present sources are identified by looking at the sub-dictionaries automatically selected in the coding. The collaborative filtering in C-HiLasso takes advantage of the temporal/spatial redundancy in the mixed signals, letting collections of samples collaborate in identifying the classes, while allowing individual samples to have different internal sparse representations. This collaboration is critical to further stabil...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Pablo Sprechmann, Ignacio Ramírez, Pablo Cancela, Guillermo Sapiro
• Keywords: Collaborative, ICASSP 2011, Mixed Signals, Signal Processing, Sparse Representation