Paper Archive

Memoryless waveshapers are commonly used in audio signal processing. In discrete time, they suffer from well-known aliasing artifacts. We present a method for applying antiderivative antialising (ADAA), which mitigates aliasing, to any waveshaping function that can be represented as a piecewise polynomial. Specifically, we treat the special case of a piecewise linear waveshaper. Furthermore, we introduce a method for for replacing the sharp corners and jump discontinuities in any piecewise linear waveshaper with smoothed polynomial approximations, whose derivatives match the adjacent line segments up to a specified order. This piecewise polynomial can again be antialiased as a special case of the general piecewise polynomial. Especially when combined with light oversampling, these techniques are effective at reducing aliasing and the proposed method for rounding corners in piecewise linear waveshapers can also create more “realistic” analog-style waveshapers than standard piecewise linear functions.

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In recent years, virtual analog modeling with neural networks experienced an increase in interest and popularity. Many different modeling approaches have been developed and successfully applied. In this paper we do not propose a novel model architecture, but rather address the problem of aliasing distortion introduced from nonlinearities of the modeled analog circuit. In particular, we propose to apply the general idea of antiderivative antialiasing to a state-trajectory network (STN). Applying antiderivative antialiasing to a stateful system in general leads to an integral of a multivariate function that can only be solved numerically, which is too costly for real-time application. However, an adapted STN can be trained to approximate the solution while being computationally efficient. It is shown that this approach can decrease aliasing distortion in the audioband significantly while only moderately oversampling the network in training and inference.

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