Underdetermined sparse source separation of convolutive mixtures with observation vector clustering
Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji Makino · 2006
We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the observation vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 times 4, 4 times 5 (#sensors times #sources) in a room (RT60= 120 ms)