English

Phase Unmixing : Multichannel Source Separation with Magnitude Constraints

Sound 2017-03-21 v2 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.1609.09744,
  title  = {Phase Unmixing : Multichannel Source Separation with Magnitude Constraints},
  author = {Antoine Deleforge and Yann Traonmilin},
  journal= {arXiv preprint arXiv:1609.09744},
  year   = {2017}
}

Comments

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States

R2 v1 2026-06-22T16:06:42.883Z