Anti-aliasing and reconstruction filter design for multi-channel active noise control window

This dissertation focuses on designing the required and reliable reconstruction filter circuit module and multi-channel Filtered-x Least Mean Square (FxLMS) algorithm module of an Active Noise Control (ANC) window system. These modules play an important role in the functional implementation of the e...

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Main Author: Liu, Yiran
Other Authors: Gan Woon Seng
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/175682
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1756822024-05-03T15:45:47Z Anti-aliasing and reconstruction filter design for multi-channel active noise control window Liu, Yiran Gan Woon Seng School of Electrical and Electronic Engineering EWSGAN@ntu.edu.sg Engineering This dissertation focuses on designing the required and reliable reconstruction filter circuit module and multi-channel Filtered-x Least Mean Square (FxLMS) algorithm module of an Active Noise Control (ANC) window system. These modules play an important role in the functional implementation of the entire ANC window system. The dissertation firstly introduces the development and application of ANC technology through literature review. Then, the theoretical basis of filter and adaptive algorithm are discussed in depth, their necessary principles and mathematical formulas are described. Based on these, a reliable and stable reconstruction filter circuit module and multi-channel FxLMS module that meets the requirements of ANC window system are designed and implemented. In order to evaluate the feasibility of the design of the reconstruction filter circuit module and the multi-channel FxLMS algorithm module, the dissertation simulates the reconstruction filter circuit using TINA and LTspice, and applies the collected real-world noise signals to the simulation experiments of the multi-channel FxLMS algorithm training module, which evaluated the effectiveness of the training module under different noise conditions. After evaluation, the experimental results show that the reconstruction filter circuit module is able to achieve the task of converting the digital audio signals generated by the ANC window system via the digital signal processing (DSP) link into analogue signals, and the model trained by the multi-channel FxLMS algorithm training module is able to effectively attenuate the noise during the whole experiment. In conclusion, the design and implementation of these two modules are based on the realisation of the functional requirements with excellent performance, perfecting the construction of the ANC window system. Master's degree 2024-05-03T02:09:24Z 2024-05-03T02:09:24Z 2024 Thesis-Master by Coursework Liu, Y. (2024). Anti-aliasing and reconstruction filter design for multi-channel active noise control window. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175682 https://hdl.handle.net/10356/175682 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Liu, Yiran
Anti-aliasing and reconstruction filter design for multi-channel active noise control window
description This dissertation focuses on designing the required and reliable reconstruction filter circuit module and multi-channel Filtered-x Least Mean Square (FxLMS) algorithm module of an Active Noise Control (ANC) window system. These modules play an important role in the functional implementation of the entire ANC window system. The dissertation firstly introduces the development and application of ANC technology through literature review. Then, the theoretical basis of filter and adaptive algorithm are discussed in depth, their necessary principles and mathematical formulas are described. Based on these, a reliable and stable reconstruction filter circuit module and multi-channel FxLMS module that meets the requirements of ANC window system are designed and implemented. In order to evaluate the feasibility of the design of the reconstruction filter circuit module and the multi-channel FxLMS algorithm module, the dissertation simulates the reconstruction filter circuit using TINA and LTspice, and applies the collected real-world noise signals to the simulation experiments of the multi-channel FxLMS algorithm training module, which evaluated the effectiveness of the training module under different noise conditions. After evaluation, the experimental results show that the reconstruction filter circuit module is able to achieve the task of converting the digital audio signals generated by the ANC window system via the digital signal processing (DSP) link into analogue signals, and the model trained by the multi-channel FxLMS algorithm training module is able to effectively attenuate the noise during the whole experiment. In conclusion, the design and implementation of these two modules are based on the realisation of the functional requirements with excellent performance, perfecting the construction of the ANC window system.
author2 Gan Woon Seng
author_facet Gan Woon Seng
Liu, Yiran
format Thesis-Master by Coursework
author Liu, Yiran
author_sort Liu, Yiran
title Anti-aliasing and reconstruction filter design for multi-channel active noise control window
title_short Anti-aliasing and reconstruction filter design for multi-channel active noise control window
title_full Anti-aliasing and reconstruction filter design for multi-channel active noise control window
title_fullStr Anti-aliasing and reconstruction filter design for multi-channel active noise control window
title_full_unstemmed Anti-aliasing and reconstruction filter design for multi-channel active noise control window
title_sort anti-aliasing and reconstruction filter design for multi-channel active noise control window
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175682
_version_ 1814047057182195712