Effective Blind Source Separation Based on the Adam Algorithm

TL;DR

This paper introduces a modified InfoMax algorithm for blind source separation that leverages the Adam stochastic optimization method, enhancing convergence and performance in signal separation tasks.

Contribution

It presents a novel integration of the Adam algorithm into the InfoMax framework for blind source separation, combining stochastic optimization with natural gradient methods.

Findings

The proposed approach improves separation quality.

Experimental results demonstrate enhanced convergence.

The method outperforms traditional algorithms in BSS tasks.

Abstract

In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods. The proposed approach is based on a novel stochastic optimization approach known as the Adaptive Moment Estimation (Adam) algorithm. The proposed BSS solution can benefit from the excellent properties of the Adam approach. In order to derive the new learning rule, the Adam algorithm is introduced in the derivation of the cost function maximization in the standard InfoMax algorithm. The natural gradient adaptation is also considered. Finally, some experimental results show the effectiveness of the proposed approach.