Blind separation of noisy piecewise-stationary mixtures via probability measure transform

Talia Ben Guy, Koby Todros

Research output: Contribution to journalArticlepeer-review


In this paper, we consider the problem of blind source separation (BSS) under non-Gaussian impulsive noise. We consider the case of overdetermined instantaneous-linear-mixtures of piecewise-stationary signals. These are corrupted by additive stationary noise. Under this framework, we propose a two-stage separation method, called measure-transformed BSS (MT-BSS), that applies a transform to the probability distribution associated with each data segment. The generating function of the transform at hand is a non-negative function, called MT-function, that weights the data points. We show that proper choice of the involved MT-functions can lead to enhanced separation performance. The performance advantage of MT-BSS over alternative BSS techniques is illustrated in simulation examples. In these studies, we consider synthetic data and real audio signals.

Original languageEnglish
Article number108967
JournalSignal Processing
StatePublished - 1 Jul 2023


  • Blind source separation
  • Parameter estimation
  • Probability measure transform
  • Robust statistics

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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