Morphological diversity and source separation

Morphological Diversity and Sparsity in Blind Source Separation

This paper describes a new blind source separation method for instantaneous linear mixtures. This new method coined GMCA (Generalized Morphological Component Analysis) relies on morphological diversity. It provides new insights on the use of sparsity for blind source separation in a noisy environment. GMCA takes advantage of the sparse representation of structured data in large overcomplete sig...

Blind Source Separation Based on Space-time-frequency Diversity

We investigate the assumption that sources have disjoint support in the time domain, time-frequency domain, or frequency domain. We call such signals disjoint orthogonal. The class of signals that approximately satisfies this assumption includes many synthetic signals, music and speech, as well as some biological signals. We measure the disjoint orthogonality of the benchmark signals in the ICA...

Source Separation, Removal, and Resynthesis Using Azimuth-based Source Separation

Source separation

Our group attracted participants from various research areas to discuss aspects of source separation for audio signals, performed either in a blind way or by using additional knowledge about the underlying sources or the mixing process. In many source separation approaches, one assumes that sources are independent, uncorrelated, and do not overlap with regard to a given representation. Also one...

Geometric source separation: merging convolutive source separation with geometric beamforming

Blind source separation of broad band signals in a multi-path environment remains a di cult problem. Robustness has been limited due to frequency permutation ambiguities. Increasing the number of sensors allows improved performance but introduces degrees of freedom in the separating lters that are not determined by separation criteria. We propose to further shape the lters and improve the robus...