Directional Selective Fixed-Filter Active Noise Control Based on a Convolutional Neural Network in Reverberant Environments

TL;DR

This paper introduces a CNN-based directional fixed-filter active noise control method that effectively estimates noise source direction and enhances noise cancellation in reverberant indoor environments, outperforming traditional adaptive algorithms.

Contribution

It presents a novel learning-based approach that incorporates noise source direction estimation into fixed-filter ANC, improving performance in reverberant conditions.

Findings

Superior noise reduction compared to traditional methods

Faster response times in reverberant environments

Effective estimation of noise source direction

Abstract

Selective fixed-filter active noise control (SFANC) is a novel approach capable of mitigating noise with varying frequency characteristics. It offers faster response and greater computational efficiency compared to traditional adaptive algorithms. However, spatial factors, particularly the influence of the noise source location, are often overlooked. Some existing studies have explored the impact of the direction-of-arrival (DoA) of the noise source on ANC performance, but they are mostly limited to free-field conditions and do not consider the more complex indoor reverberant environments. To address this gap, this paper proposes a learning-based directional SFANC method that incorporates the DoA of the noise source in reverberant environments. In this framework, multiple reference signals are processed by a convolutional neural network (CNN) to estimate the azimuth and elevation angles of the noise source, as well as to identify the most appropriate control filter for effective noise cancellation. Compared to traditional adaptive algorithms, the proposed approach achieves superior noise reduction with shorter response times, even in the presence of reverberations.