Antiderivative antialiasing for memoryless nonlinearities

Stefan Bilbao, Fabian Esqueda, Julian Parker, Vesa Valimaki

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 is based on higher order antiderivatives of the nonlinear function used. The first antiderivative form of the new method is equivalent to a technique proposed recently by Parker {\it et al}. The second and third order cases of the new method offer considerable improvement over the first antiderivative method, improving the signal-to-noise ratio in comparison to a straightforward implementation. 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
JournalIEEE Signal Processing Letters
VolumePP
Issue number99
Early online date1 Mar 2017
DOIs
Publication statusE-pub ahead of print - 1 Mar 2017

Keywords / Materials (for Non-textual outputs)

  • signal processing algorithms
  • aliasing
  • harmonic distortion
  • nonlinear systems
  • signal denoising

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