Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control

TL;DR

This paper introduces a novel meta-learning approach using a complex self-attention neural network for delayless subband adaptive filtering in active noise control, improving robustness and efficiency in nonstationary and nonlinear environments.

Contribution

It proposes a meta-learning-based delayless subband adaptive filter with a self-attention neural network, enabling adaptive noise control in complex environments without additional delay.

Findings

Achieves superior noise reduction compared to traditional methods.

Demonstrates robustness across various noise conditions.

Enables resource-efficient updates with skip updating strategy.

Abstract

Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in addressing nonlinear environments and nonstationary noise. To tackle this challenge, we reformulate the active noise control problem as a meta-learning problem and propose a meta-learning-based delayless subband adaptive filter with deep neural networks. The core idea is to utilize a neural network as an adaptive algorithm that can adapt to different environments and types of noise. The neural network will train under noisy observations, implying that it recognizes the optimized updating rule without true labels. A single-headed attention recurrent neural network is devised with learnable feature embedding to update the adaptive filter weight efficiently, enabling accurate computation of the secondary source to attenuate the unwanted primary noise. In order to relax the time constraint on updating the adaptive filter weights, the delayless subband architecture is employed, which will allow the system to be updated less frequently as the downsampling factor increases. In addition, the delayless subband architecture does not introduce additional time delays in active noise control systems. A skip updating strategy is introduced to decrease the updating frequency further so that machines with limited resources have more possibility to board our meta-learning-based model. Extensive multi-condition training ensures generalization and robustness against various types of noise and environments. Simulation results demonstrate that our meta-learning-based model achieves superior noise reduction performance compared to traditional methods.