Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Survey of Integrating Wireless Technology into Active Noise Control

Xiaoyi Shen, Dongyuan Shi, Zhengding Luo, Junwei Ji, Woon-Seng Gan

TL;DR

This survey analyzes how wireless technologies can augment active noise control by delivering higher-quality reference signals and distributing computation. It catalogs multiple algorithms—from lookahead and cloud-assisted FxLMS to coherence-based reference selection and hybrid schemes—that improve convergence speed and noise reduction while managing computation and synchronization challenges. Key contributions include coherence-based selection/weighting, digital-twin FxLMS, and error-separation approaches for robust performance across earbuds, headrests, and windows. The work highlights practical implications for consumer devices and smart environments as IoT connectivity enables broader deployment of wireless ANC.

Abstract

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system, equipped with some innovative algorithms, in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings.

A Survey of Integrating Wireless Technology into Active Noise Control

TL;DR

This survey analyzes how wireless technologies can augment active noise control by delivering higher-quality reference signals and distributing computation. It catalogs multiple algorithms—from lookahead and cloud-assisted FxLMS to coherence-based reference selection and hybrid schemes—that improve convergence speed and noise reduction while managing computation and synchronization challenges. Key contributions include coherence-based selection/weighting, digital-twin FxLMS, and error-separation approaches for robust performance across earbuds, headrests, and windows. The work highlights practical implications for consumer devices and smart environments as IoT connectivity enables broader deployment of wireless ANC.

Abstract

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system, equipped with some innovative algorithms, in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings.
Paper Structure (12 sections, 29 equations, 5 figures, 1 table)

This paper contains 12 sections, 29 equations, 5 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Wireless technique integrated with ANC
  3. Filtered-X least mean square algorithm
  4. Wireless lookahead aware ANC (WLANC)
  5. Wireelss networked adaptive ANC with digital-twin FXLMS algorithm (WANC-DT)
  6. Wireless ANC with coherence-based-selection technique (WANC-CBS)
  7. Wireless ANC with coherence-based weight determination technique (WANC-CWD)
  8. Wireless hybrid ANC with error separation module (WHANC-ESM)
  9. Wireless hybrid ANC with fixed-adaptive control selection technique (WHANC-FAS)
  10. Comparative evaluation of wireless ANC
  11. Applications of wireless ANC
  12. Conclusion

Figures (5)

  • Figure 1: The block diagram of wireless networked adaptive ANC with digital-twin FXLMS algorithm
  • Figure 2: The block diagram of wireless ANC implemented with coherence-based selection method.
  • Figure 3: The block diagram of wireless ANC implemented with coherence-based weight determination method.
  • Figure 4: The block diagram of wireless hybrid ANC implemented with error separation module.
  • Figure 5: The block diagram of wireless hybrid ANC implemented with fixed-adaptive control selection.