Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters

TL;DR

This paper introduces Latent FxLMS, a neural network-based enhancement to the traditional FxLMS algorithm for active noise control, which accelerates convergence by leveraging low-dimensional manifold learning of filter weights.

Contribution

The paper proposes a novel neural adaptive filter framework that constrains FxLMS weights to a learned low-dimensional manifold, improving convergence speed while maintaining steady-state accuracy.

Findings

Latent FxLMS converges faster than standard FxLMS under certain conditions.

The method maintains comparable steady-state mean squared error.

Neural constraints and normalization techniques influence convergence and accuracy.

Abstract

Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS.