# Deep Generative Fixed-filter Active Noise Control

arXiv: https://arxiv.org/abs/2303.05788 · HTML: https://mlsanity.com/paper/2303.05788/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 10 Mar 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [Urban Sound Tagging using Convolutional Neural Networks](https://mlsanity.com/paper/1909.12699/) (2019)
- [A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning](https://mlsanity.com/paper/2208.08082/) (2022)
- [Spatial active noise control based on individual kernel interpolation of primary and secondary sound fields](https://mlsanity.com/paper/2202.04807/) (2022)

## Built on by (most cited first, 5 of 5)

- [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)
- [PRIME-ANC: Path-Ratio-Informed Modeling for Efficient Neural Filter Synthesis in Active Noise Control](https://mlsanity.com/paper/2609.31772/) (2026)

## Abstract (arXiv)

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.