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Antiderivative antialiasing (ADAA) has emerged as a recent approach to reduce aliases for mathematically defined nonlinearities. In this study, ADAA is applied to simplified nonlinear Volterra modeling, which is a method for blackbox modeling of Hammerstein nonlinearities. Previously reported ADAA approaches contain a variable difference term in the denominator and therefore rely on a continuous piecewise function to prevent very small denominators. However, when applied to simplified Volterra models, this denominator term is eliminated, resulting in a polynomial function. This polynomial ADAA was tested against the standard approach of low-pass filtering the input to prevent aliasing. It was found that these two approaches perform comparably but that by combining them together, superior alias reduction can be achieved.
Author (s): Bennett, Christopher; Hopman, Stefan
Affiliation:
Music Engineering Technology, Frost School of Music, University of Miami, Coral Gables, FL
(See document for exact affiliation information.)
Publication Date:
2022-09-06
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Permalink: https://aes2.org/publications/elibrary-page/?id=21882
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Bennett, Christopher; Hopman, Stefan; 2022; Antialiasing for Simplified Nonlinear Volterra Models [PDF]; Music Engineering Technology, Frost School of Music, University of Miami, Coral Gables, FL; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=21882
Bennett, Christopher; Hopman, Stefan; Antialiasing for Simplified Nonlinear Volterra Models [PDF]; Music Engineering Technology, Frost School of Music, University of Miami, Coral Gables, FL; Paper ; 2022 Available: https://aes2.org/publications/elibrary-page/?id=21882