Graph Signal Processing Meets Blind Source Separation

by   Jari Miettinen, et al.

In graph signal processing (GSP), prior information on the dependencies in the signal is collected in a graph which is then used when processing or analyzing the signal. Blind source separation (BSS) techniques have been developed and analyzed in different domains, but for graph signals the research on BSS is still in its infancy. In this paper, this gap is filled with two contributions. First, a nonparametric BSS method, which is relevant to the GSP framework, is refined, the Cramér-Rao bound (CRB) for mixing and unmixing matrix estimators in the case of Gaussian moving average graph signals is derived, and for studying the achievability of the CRB, a new parametric method for BSS of Gaussian moving average graph signals is introduced. Second, we also consider BSS of non-Gaussian graph signals and two methods are proposed. Identifiability conditions show that utilizing both graph structure and non-Gaussianity provides a more robust approach than methods which are based on only either graph dependencies or non-Gaussianity. It is also demonstrated by numerical study that the proposed methods are more efficient in separating non-Gaussian graph signals.



There are no comments yet.


page 1

page 2

page 3

page 4


Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix

Unsupervised single-channel blind source separation is a long standing s...

Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure

Blind source separation (BSS) is one of the most important and establish...

Convergent Bayesian formulations of blind source separation and electromagnetic source estimation

We consider two areas of research that have been developing in parallel ...

Blind Source Separation: Fundamentals and Recent Advances (A Tutorial Overview Presented at SBrT-2001)

Blind source separation (BSS), i.e., the decoupling of unknown signals t...

Generalized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations

Non-orthogonal joint diagonalization (NJD) free of prewhitening has been...

Power Systems Topology and State Estimation by Graph Blind Source Separation

In this paper, we consider the problem of blind estimation of states and...

Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations

This paper addresses the problem of blind demixing of instantaneous mixt...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.