Practical Active Noise Control Algorithms in Bayesian Inversion Framework
This paper approaches the problem of adaptive active noise control (ANC) as a Bayesian inverse problem. Initially, a forward model for the ANC system in the generic form usually used in the theory of inverse problems is derived. A vector of control system parameters is considered the problem's...
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| Veröffentlicht in: | 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) S. 1 - 6 |
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| Sprache: | Englisch |
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16.11.2022
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| Abstract | This paper approaches the problem of adaptive active noise control (ANC) as a Bayesian inverse problem. Initially, a forward model for the ANC system in the generic form usually used in the theory of inverse problems is derived. A vector of control system parameters is considered the problem's unknown variable. The unknown is assumed to be a multivariate random variable with a Gaussian prior probability density function. A data vector is formed using samples of the residual noise signal collected by a feedback microphone. Then, the standard Bayesian inversion method is applied to the forward model, resulting in a posterior probability density function for the unknown variable. We use the maximizer of this function to adjust the ANC system parameters. Both numerical results using computer simulation and empirical results using an experimental ANC setup confirm the efficiency of the proposed algorithm in practice. |
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| AbstractList | This paper approaches the problem of adaptive active noise control (ANC) as a Bayesian inverse problem. Initially, a forward model for the ANC system in the generic form usually used in the theory of inverse problems is derived. A vector of control system parameters is considered the problem's unknown variable. The unknown is assumed to be a multivariate random variable with a Gaussian prior probability density function. A data vector is formed using samples of the residual noise signal collected by a feedback microphone. Then, the standard Bayesian inversion method is applied to the forward model, resulting in a posterior probability density function for the unknown variable. We use the maximizer of this function to adjust the ANC system parameters. Both numerical results using computer simulation and empirical results using an experimental ANC setup confirm the efficiency of the proposed algorithm in practice. |
| Author | Pour, Soheil Ardekani, Iman Sharifzadeh, Hamid |
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| Snippet | This paper approaches the problem of adaptive active noise control (ANC) as a Bayesian inverse problem. Initially, a forward model for the ANC system in the... |
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| SubjectTerms | active noise control adaptive control Bayes methods Bayesian inversion Computational modeling Computer simulation Control systems Inverse problems Measurement uncertainty Probability density function |
| Title | Practical Active Noise Control Algorithms in Bayesian Inversion Framework |
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