Subband Architecture Aided Selective Fixed-Filter Active Noise Control

TL;DR

This paper introduces a subband-based fixed-filter approach for active noise control that improves convergence speed, noise reduction effectiveness, and robustness in complex environments by pre-training subband filters and combining them during real-time operation.

Contribution

It proposes a novel subband structure for fixed-filter active noise control, enabling better handling of diverse and complex noise spectra compared to existing methods.

Findings

Fast convergence and effective noise suppression demonstrated

Robust performance in complex noisy environments

Pre-trained subband filters improve adaptability

Abstract

The feedforward selective fixed-filter method selects the most suitable pre-trained control filter based on the spectral features of the detected reference signal, effectively avoiding slow convergence in conventional adaptive algorithms. However, it can only handle limited types of noises, and the performance degrades when the input noise exhibits non-uniform power spectral density. To address these limitations, this paper devises a novel selective fixed-filter scheme based on a delayless subband structure. In the off-line training stage, subband control filters are pre-trained for different frequency ranges and stored in a dedicated sub-filter database. During the on-line control stage, the incoming noise is decomposed using a polyphase FFT filter bank, and a frequency-band-matching mechanism assigns each subband signal the most appropriate control filter. Subsequently, a weight stacking technique is employed to combine all subband weights into a fullband filter, enabling real-time noise suppression. Experimental results demonstrate that the proposed scheme provides fast convergence, effective noise reduction, and strong robustness in handling more complicated noisy environments.