On convolutive Blind Source Separation in a noisy context and a total variation regularization

Abstract

We propose a new strategy for improving classical Blind Source Separation (BSS) methods. This strategy consists in denoising both the observed and the estimated source signals, and is based on the minimization of a regularized criterion which takes into account the Total Variation of the signal. We prove by the way that the method leads to a projection problem which is solved by means of projected gradient algorithm. The effectiveness and the robustness of the proposed separating process are shown on numerical examples.

Effective Blind Source Separation Based on the Adam Algorithm

“…Several well-performing approaches exist when the mixing environment is instantaneous [7,3], while some problems still arise in convolutive environments [2,4,17]. Different approaches were proposed to solve BSS in linear and instantaneous environment.…”