Antiderivative Antialiasing for Chebyshev Based Generalized Hammerstein Models

Abstract

Antiderivative antialiasing (ADAA) has proven to be an effective approach for reducing aliasing in mathematically defined nonlinear functions. This paper explores the application of ADAA to Chebyshev-based generalized Hammerstein models, which are utilized for blackbox modeling of nonlinearities in digital audio effects. The Chebyshev-based model eliminates certain matrix operations and therefore offers advantages over polynomial-based models. By integrating ADAA, this enhanced Chebyshev model achieves substantial aliasing reductions, comparable to upsampling. Both explicit and recursive implementations of a Chebyshev model are developed and evaluated for alias reduction, waveshape fidelity, and computational efficiency. The results demonstrate the potential of ADAA to enhance Chebyshev polynomials for modeling of nonlinear systems, making it a valuable technique for real-time audio processing.

Authors

• T. C. Baker
• Christopher J. Bennett