Predictive Directional Selective Fixed-Filter Active Noise Control for Moving Sources via a Convolutional Recurrent Neural Network

TL;DR

This paper introduces a CRNN-based predictive noise control method that enhances tracking and cancellation of moving noise sources by forecasting future noise directions.

Contribution

It proposes a novel PD-SFANC approach utilizing CRNNs to predict control filters for moving noise sources, improving dynamic noise reduction performance.

Findings

Numerical simulations show improved noise tracking for moving sources.

The method outperforms several baseline ANC methods.

Enhanced dynamic noise reduction in various movement scenarios.

Abstract

Directional Selective Fixed-Filter Active Noise Control (D-SFANC) can effectively attenuate noise from different directions by selecting the suitable pre-trained control filter based on the Direction-of-Arrival (DoA) of the current noise. However, this method is weak at tracking the direction variations of non-stationary noise, such as that from a moving source. Therefore, this work proposes a Predictive Directional SFANC (PD-SFANC) method that uses a Convolutional Recurrent Neural Network (CRNN) to capture the hidden temporal dynamics of the moving noise and predict the control filter to cancel future noise. Accordingly, the proposed method can significantly improve its noise-tracking ability and dynamic noise-reduction performance. Furthermore, numerical simulations confirm the superiority of the proposed method for handling moving sources across various movement scenarios, compared to several representative ANC baselines.