# A survey on active noise control techniques -- Part I: Linear systems

arXiv: https://arxiv.org/abs/2110.00531 · HTML: https://mlsanity.com/paper/2110.00531/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 551 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximation](https://mlsanity.com/paper/1804.00462/) (2018)
- [Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents](https://mlsanity.com/paper/1812.09985/) (2018)
- [Mode Domain Spatial Active Noise Control Using Sparse Signal Representation](https://mlsanity.com/paper/1803.00187/) (2018)
- [Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm](https://mlsanity.com/paper/1308.3300/) (2013)
- [Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm](https://mlsanity.com/paper/1507.05233/) (2015)

## Built on by (most cited first, 2 of 2)

- [A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning](https://mlsanity.com/paper/2208.08082/) (2022)
- [Effects of Dataset Sampling Rate for Noise Cancellation through Deep Learning](https://mlsanity.com/paper/2405.20884/) (2024)

## Abstract (arXiv)

Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)-based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.