Transformer-based End-to-End Control Filter Generation for Active Noise Control

TL;DR

This paper introduces a Transformer-based end-to-end framework for active noise control that directly generates control filters, improving noise reduction and adaptability without relying on supervised learning or filter decomposition.

Contribution

It presents a novel unsupervised, end-to-end control filter generation method using Transformer architecture, simplifying the control pipeline and enhancing performance over traditional GFANC methods.

Findings

Achieves better noise reduction compared to GFANC.

Demonstrates improved adaptability to various noise types.

Simplifies the control process by removing decomposition steps.

Abstract

To address the limitations of existing Generative Fixed-Filter Active Noise Control (GFANC) methods, which rely on filter decomposition and recombination and require supervised learning with labeled data, this paper proposes a Transformer-based End-to-End Control-Filter Generation (E2E-CFG) framework. Unlike previous approaches that predict combination weights of sub control filters, the proposed method directly generates control filters in an unsupervised manner by integrating the co-processor and real-time controller into a fully differentiable ANC system, where the accumulated error signal is used as the training objective. By abandoning the decomposition--reconstruction process, the proposed design simplifies the control pipeline and avoids error accumulation, while the Transformer architecture effectively captures global and dynamic noise characteristics through its attention mechanism. Numerical simulations on real-recorded noises demonstrate that the proposed method achieves improved noise reduction performance and adaptability to different types of noises compared with the original GFANC framework.