Projection design for compressive source separation using mean errors and cross-validation

Ajit Rajwade

Abstract:

This paper addresses the task of projection design for
source separation in the compressive domain, where one observes a compressed linear mixture of two source signals with
known priors. By positioning that both the sources follow a
Gaussian mixture, we formulate an objective that tightly approximates the minimum mean squared error and solve the
optimization problem using a gradient-based approach. In the
blind setting, where the mixing ratio unknown, we propose a
cross-validation approach to independently estimate the mixing ratio. We also provide a number of numerical results on
synthetic and real image data that validate our findings. To
the best of our knowledge, this is the first effort in projection
design for prior-based source separation