Blind source separation based on time-frequency signal representations

TLDR

Blind source separation aims to recover individual signals from instantaneous linear mixtures, traditionally relying on statistical properties of the sources. This study introduces a new BSS method that exploits differences in time‑frequency signatures and presents asymptotic analysis and simulations. The method diagonalizes a combined set of spatial time‑frequency distributions to separate the sources. It can separate Gaussian sources with identical spectral shapes but distinct time‑frequency localization, and spreading noise power while localizing source energy enhances robustness and performance.

Abstract

Blind source separation consists of recovering a set of signals of which only instantaneous linear mixtures are observed. Thus far, this problem has been solved using statistical information available on the source signals. This paper introduces a new blind source separation approach exploiting the difference in the time-frequency (t-f) signatures of the sources to be separated. The approach is based on the diagonalization of a combined set of "spatial t-f distributions". In contrast to existing techniques, the proposed approach allows the separation of Gaussian sources with identical spectral shape but with different t-f localization properties. The effects of spreading the noise power while localizing the source energy in the t-f domain amounts to increasing the robustness of the proposed approach with respect to noise and, hence, improved performance. Asymptotic performance analysis and numerical simulations are provided.