Diffusion leaky LMS algorithm: analysis and implementation
Incremental improvement to diffusion LMS for speech noise suppression, a specific domain application.
The authors propose a diffusion leaky LMS algorithm to improve numerical stability and reduce misadjustment for noisy speech signals in distributed estimation, demonstrating its effectiveness in a noise reduction network.
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.