Feedback-Guided DNN-Based Controller Fusion for Robust Fixed-Parameter Active Noise Control

Abstract

In active noise control (ANC) systems, adaptive approaches may suffer from instability or divergence, limiting their practical deployment. Consequently, fixed-parameter controllers are widely adopted, but their performance degrades under varying noise characteristics and acoustic path conditions. This paper proposes a feedback-guided DNN-based controller fusion framework for robust fixed-parameter ANC. The proposed method combines a causal WaveNet controller with a feedback-guided mixture-of-experts (MoE) module, where a gating network estimates the weights of multiple pre-trained FIR experts according to the current acoustic condition. The proposed approach improves robustness to varying acoustic conditions without online parameter updating. Furthermore, the model is fully causal and supports sample-wise streaming inference, with computational costs evenly distributed across sampling points to reduce peak computational load. Experimental results on headphone ANC demonstrate substantial low-frequency noise reduction with negligible noise amplification over 1-8 kHz.