Method of Noise-Robust Estimation of Parameters of an Autoregressive Model in the Frequency Domain

The article considers the problem of estimating the parameters of the autoregressive (AR) signal in the presence of background noise. Based on the frequency representation of the AR signal, a technique of calculating the likelihood function of the AR parameters is shown and the implementation of the...

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Bibliographic Details
Published in:Cybernetics and systems analysis 2021-09, Vol.57 (5), p.836-842
Main Authors: Zadiraka, V. K., Semenov, V. Yu, Semenova, Ye. V.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Autoregressive models
Autoregressive processes
Background noise
Computer Engineering
Control
Frequency domain analysis
Informatics
Iterative methods
Mathematics
Mathematics and Statistics
New Means of Cybernetics
Parameter robustness
Processor Architectures
Software Engineering/Programming and Operating Systems
Systems Analysis
Systems Theory
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Summary:The article considers the problem of estimating the parameters of the autoregressive (AR) signal in the presence of background noise. Based on the frequency representation of the AR signal, a technique of calculating the likelihood function of the AR parameters is shown and the implementation of the Expectation-Maximization method for iterative evaluation of the AR parameters is considered. Analysis of different measures of distortion of speech signals shows that the proposed approaches in the frequency domain have the same accuracy as the corresponding approaches in the time domain, but are characterized by significantly lower computing costs.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-021-00409-y