2016/09/30 by Antoine Deleforge, Deleforge, Antoine, Yann Traonmilin +1
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Sound (cs.SD) #cs.SD #stat.ML
paper · pdf · doi:10.48550/arxiv.1609.09744
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States
arxiv created 2017/03/20 · arxiv updated 2017/03/21
We consider the problem of estimating the phases of K mixed complex signals from a multichannel observation, when the mixing matrix and signal magnitudes are known. This problem can be cast as a non-convex quadratically constrained quadratic program which is known to be NP-hard in general. We propose three approaches to tackle it: a heuristic method, an alternate minimization method, and a convex relaxation into a semi-definite program. The last two approaches are showed to outperform the oracle multichannel Wiener filter in under-determined informed source separation tasks, using simulated and speech signals. The convex relaxation approach yields best results, including the potential for exact source separation in under-determined settings.