Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166615
Title: Advanced active noise control headphone: algorithm and implementation
Authors: Shen, Xiaoyi
Keywords: Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Shen, X. (2023). Advanced active noise control headphone: algorithm and implementation. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166615
Abstract: Active noise control (ANC) is becoming increasingly important in daily life with the growing desire for a quiet environment. ANC headphones are commonly used to reduce noise while working, studying, or relaxing. A traditional ANC headphone generates the anti-noise with reference microphones, error microphones, and secondary sources, which have the same amplitude but opposite phase as the noise. However, some practical challenges in ANC headphones must be overcome, such as adaptive algorithm instability and computation complexity consideration, dynamic noise reduction in the multi-noise environment, reference signal with low reference to interference ratio, and causality constraints. This thesis presents some algorithms and implementations for dealing with these difficulties and improving the noise reduction performance of ANC headphones. An adaptive gain (AG) algorithm combining fixed filters with adaptive theory together with the multi-reference strategy is proposed in the thesis. The main motivation of the AG algorithm is to allow a larger step size to be applied compared to the FXLMS algorithm and result in faster convergence. Theoretical analysis of the proposed AG algorithm's step size bounds and convergence behavior is performed, and simulations and real-time experiments validate the AG algorithm's noise reduction performance. The reference microphone is usually mounted on the earcup. It collects all the ambient noise surrounding the earcup, including some incoherent noise with the disturbance, thereby degrading the ANC system's noise reduction. In this thesis, the wireless microphones are placed in front of the noise sources we target to attenuate in the multi-noise environment to acquire the reference signal with a high reference-to-interference ratio. The look-ahead time provided by wireless transmission benefits the implementation of causality constraints in ANC. The thesis also proposes a wireless feedforward ANC with coherence-based selection to select the most coherent input signal to the ANC controller for the anti-noise generation. Furthermore, a multi-channel wireless ANC is extended with the coherence-based weight determination algorithm to improve the noise reduction performance of wireless feedforward ANC. One of the difficulties associated with wireless feedforward ANC is the requirement for prior knowledge of the reference signal. When an unexpected interference occurs in the environment not picked up by the reference microphone, the noise reduction capability of wireless feedforward ANC degrades dramatically. A hybrid ANC structure with an alternating switching method is proposed to address this practical issue. With the application of wireless techniques, this thesis also proposes a multi-channel wireless hybrid ANC with fixed-adaptive control selection to further enhance the noise reduction capability of the ANC headphone. Another possibility is to combine wireless and earcup microphones on the headphone. A module for error separation is used in conjunction with the wireless hybrid ANC to enhance convergence performance.
URI: https://hdl.handle.net/10356/166615
DOI: 10.32657/10356/166615
Schools: School of Electrical and Electronic Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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