An investigation of delayless subband adaptive filtering for multi-input multi-output active noise control applications
An investigation of delayless subband adaptive filtering for multi-input multi-output active noise control applications
The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, single- output broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance. The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation
359-373
Cheer, Jordan
8e452f50-4c7d-4d4e-913a-34015e99b9dc
Daley, Stephen
53cef7f1-77fa-4a4c-9745-b6a0ba4f42e6
7 December 2016
Cheer, Jordan
8e452f50-4c7d-4d4e-913a-34015e99b9dc
Daley, Stephen
53cef7f1-77fa-4a4c-9745-b6a0ba4f42e6
Cheer, Jordan and Daley, Stephen
(2016)
An investigation of delayless subband adaptive filtering for multi-input multi-output active noise control applications.
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25 (2), .
(doi:10.1109/TASLP.2016.2637298).
Abstract
The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, single- output broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance. The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation
Text
MIMO_subband_ANC.pdf
- Accepted Manuscript
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Accepted/In Press date: 5 December 2016
Published date: 7 December 2016
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© © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Signal Processing & Control Grp
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Local EPrints ID: 403637
URI: http://eprints.soton.ac.uk/id/eprint/403637
PURE UUID: 9da19645-43c6-4dd0-8827-289c3a18f1a3
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Date deposited: 08 Dec 2016 08:58
Last modified: 16 Mar 2024 04:05
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