Performance enhancement of adaptive Active Noise Control systems for FMRI machines

Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model...

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Published in:2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Vol. 2010; pp. 4327 - 4330
Main Authors: Kannan, G, Milani, A A, Panahi, I M S, Kehtarnavaz, N
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.01.2010
Subjects:
Acoustic noise
active noise control
adaptive
Convergence
FIR
Magnetic resonance imaging
Magnetic Resonance Imaging - instrumentation
Microphones
Noise
Transfer functions
ISBN:1424441234, 9781424441235
ISSN:1094-687X, 1557-170X, 2375-7477
Online Access:Get full text
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Abstract Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.
AbstractList Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.
Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.
Author Kehtarnavaz, N
Kannan, G
Panahi, I M S
Milani, A A
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SubjectTerms Acoustic noise
active noise control
adaptive
Convergence
FIR
Magnetic resonance imaging
Magnetic Resonance Imaging - instrumentation
Microphones
Noise
Transfer functions
Title Performance enhancement of adaptive Active Noise Control systems for FMRI machines
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