Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm
The multichannel active noise control (MCANC) system, in which multiple reference sensors and actuators are used to enlarge the noise-cancellation zone, is widely utilized in complex acoustic environments. However, as the number of channels increases, the practicality decreases due to the exponentia...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172783 |
| _version_ | 1811685813290795008 |
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| author | Shi, Dongyuan Lam, Bhan Ji, Junwei. Shen, Xiaoyi Lai, Chung Kwan Gan, Woon-Seng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Shi, Dongyuan Lam, Bhan Ji, Junwei. Shen, Xiaoyi Lai, Chung Kwan Gan, Woon-Seng |
| author_sort | Shi, Dongyuan |
| collection | NTU |
| description | The multichannel active noise control (MCANC) system, in which multiple reference sensors and actuators are used to enlarge the noise-cancellation zone, is widely utilized in complex acoustic environments. However, as the number of channels increases, the practicality decreases due to the exponential rise in computational complexity. This paper, therefore, revisits the adjoint least mean square (ALMS) algorithm and its multichannel applications. The computational analysis reveals that the multichannel adjoint least mean square (McALMS) algorithm1 has a significantly lower computation cost when implementing the fully connected active noise control (ANC) structure. In addition to this advantage, the theoretical analysis presented in this paper demonstrates that the McALMS algorithm can achieve the same optimal solution as the standard adaptive algorithm without the assumptions of input independence and white Gaussian noise. In addition, a practical step-size estimation strategy based on the Golden-section search (GSS) method is proposed to predict the fast step size of the McALMS algorithm. The numerical simulations in a multichannel ANC system demonstrate the effectiveness of the McALMS algorithm and validate the derived theoretical analysis. Furthermore, the McALMS algorithm with proposed step-size approach is used to implement a multichannel noise cancellation window that achieves satisfactory global noise reduction performance for tonal, broadband, and even real-world noises. |
| first_indexed | 2024-10-01T04:50:29Z |
| format | Journal Article |
| id | ntu-10356/172783 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:50:29Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1727832023-12-20T01:25:19Z Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm Shi, Dongyuan Lam, Bhan Ji, Junwei. Shen, Xiaoyi Lai, Chung Kwan Gan, Woon-Seng School of Electrical and Electronic Engineering Digital Signal Processing Laboratory Engineering::Electrical and electronic engineering Multichannel Active Noise Control Adaptive Algorithm The multichannel active noise control (MCANC) system, in which multiple reference sensors and actuators are used to enlarge the noise-cancellation zone, is widely utilized in complex acoustic environments. However, as the number of channels increases, the practicality decreases due to the exponential rise in computational complexity. This paper, therefore, revisits the adjoint least mean square (ALMS) algorithm and its multichannel applications. The computational analysis reveals that the multichannel adjoint least mean square (McALMS) algorithm1 has a significantly lower computation cost when implementing the fully connected active noise control (ANC) structure. In addition to this advantage, the theoretical analysis presented in this paper demonstrates that the McALMS algorithm can achieve the same optimal solution as the standard adaptive algorithm without the assumptions of input independence and white Gaussian noise. In addition, a practical step-size estimation strategy based on the Golden-section search (GSS) method is proposed to predict the fast step size of the McALMS algorithm. The numerical simulations in a multichannel ANC system demonstrate the effectiveness of the McALMS algorithm and validate the derived theoretical analysis. Furthermore, the McALMS algorithm with proposed step-size approach is used to implement a multichannel noise cancellation window that achieves satisfactory global noise reduction performance for tonal, broadband, and even real-world noises. Ministry of National Development (MND) National Research Foundation (NRF) This research is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office under the Cities of Tomorrow (CoT) Research Programme (CoT Award No. COT-V4-2019-1). 2023-12-20T01:25:19Z 2023-12-20T01:25:19Z 2023 Journal Article Shi, D., Lam, B., Ji, J., Shen, X., Lai, C. K. & Gan, W. (2023). Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm. Mechanical Systems and Signal Processing, 199, 110444-. https://dx.doi.org/10.1016/j.ymssp.2023.110444 0888-3270 https://hdl.handle.net/10356/172783 10.1016/j.ymssp.2023.110444 2-s2.0-85160596017 199 110444 en COT-V4-2019-1 Mechanical Systems and Signal Processing © 2023 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Multichannel Active Noise Control Adaptive Algorithm Shi, Dongyuan Lam, Bhan Ji, Junwei. Shen, Xiaoyi Lai, Chung Kwan Gan, Woon-Seng Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title | Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title_full | Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title_fullStr | Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title_full_unstemmed | Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title_short | Computation-efficient solution for fully-connected active noise control window: analysis and implementation of multichannel adjoint least mean square algorithm |
| title_sort | computation efficient solution for fully connected active noise control window analysis and implementation of multichannel adjoint least mean square algorithm |
| topic | Engineering::Electrical and electronic engineering Multichannel Active Noise Control Adaptive Algorithm |
| url | https://hdl.handle.net/10356/172783 |
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