Diffusion leaky LMS algorithm: analysis and implementation

TL;DR

This paper introduces a diffusion leaky LMS algorithm that enhances stability and reduces misadjustment in noisy speech signal processing, with practical implementations for noise reduction.

Contribution

It proposes a novel diffusion leaky dLMS algorithm that improves robustness and stability in noisy speech environments, along with two implementation methods.

Findings

Enhanced numerical stability in noisy speech scenarios

Reduced misadjustment compared to standard dLMS

Effective noise reduction in speech signal processing

Abstract

The diffusion least-mean square (dLMS) algorithms have attracted much attention owing to its robustness for distributed estimation problems. However, the performance of such filters may change when they are implemented for suppressing noises from speech signals. To overcome this problem, a diffusion leaky dLMS algorithm is proposed in this work, which is characterized by its numerical stability and small misadjustment for noisy speech signals when the unknown system is a lowpass filter. Finally, two implementations of the leaky dLMS are introduced. It is demonstrated that the leaky dLMS can be effectively introduced into a noise reduction network for speech signals.