Antiderivative Antialiasing for Memoryless Nonlinearities

Stefan Bilbao*, Fabian Esqueda Flores, Julian D. Parker, Vesa Välimäki

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
156 Downloads (Pure)

Abstract

Aliasing is a commonly encountered problem in audio signal processing, particularly when memoryless nonlinearities are simulated in discrete time. A conventional remedy is to operate at an oversampled rate. A new aliasing reduction method is proposed here for discrete-time memoryless nonlinearities, which is suitable for operation at reduced oversampling rates. The method employs higher order antiderivatives of the nonlinear function used. The first-order form of the new method is equivalent to a technique proposed recently by Parker et al. Higher order extensions offer considerable improvement over the first antiderivative method, in terms of the signal-to-noise ratio. The proposed methods can be implemented with fewer operations than oversampling and are applicable to discrete-time modeling of a wide range of nonlinear analog systems.

Original languageEnglish
Pages (from-to)1049-1053
Number of pages5
JournalIEEE Signal Processing Letters
Volume24
Issue number7
DOIs
Publication statusPublished - Jul 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Aliasing
  • harmonic distortion
  • nonlinear systems
  • signal denoising
  • signal processing algorithms
  • WAVE-FORMS
  • OSCILLATOR
  • DISTORTION
  • SIGNALS
  • FILTER

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