A survey on active noise control techniques -- Part I: Linear systems
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 551 references that are in the map. First posted on arXiv 1 Oct 2021.
Builds on
8 of 551 references.
References · 8
- 2016Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniquescited by 20
- 2014Direction Finding Algorithms with Joint Iterative Subspace Optimizationcited by 19
- 2014Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkagecited by 15
- 2018Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximationcited by 10
- 2018Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agentscited by 10
- 2018Mode Domain Spatial Active Noise Control Using Sparse Signal Representationcited by 2
- 2013Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithmcited by 1
- 2015Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithmcited by 1
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.