# A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

arXiv: https://arxiv.org/abs/2208.08082 · HTML: https://mlsanity.com/paper/2208.08082/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [A survey on active noise control techniques -- Part I: Linear systems](https://mlsanity.com/paper/2110.00531/) (2021)

## Built on by (most cited first, 7 of 7)

- [Deep Generative Fixed-filter Active Noise Control](https://mlsanity.com/paper/2303.05788/) (2023)
- [Unsupervised learning based end-to-end delayless generative fixed-filter active noise control](https://mlsanity.com/paper/2402.09460/) (2024)
- [Deep Active Speech Cancellation with Mamba-Masking Network](https://mlsanity.com/paper/2502.01185/) (2025)
- [Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation](https://mlsanity.com/paper/2505.16911/) (2025)
- [Toward Optimal ANC: Establishing Mutual Information Lower Bound](https://mlsanity.com/paper/2505.17877/) (2025)
- [Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control](https://mlsanity.com/paper/2508.13018/) (2025)
- [DOA Estimation with Lightweight Network on LLM-Aided Simulated Acoustic Scenes](https://mlsanity.com/paper/2511.08012/) (2025)

## Abstract (arXiv)

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.