Aliasing Reduction in Neural Amp Modeling by Smoothing Activations

Abstract: The increasing demand for high-quality digital emulations of analog audio hardware such as vintage guitar amplifiers has led to numerous works in neural-network-based black-box modeling, with deep learning architectures like WaveNet showing promising results. However, a key limitation in all of these models is the aliasing artifacts that arise from the use of nonlinear activation functions in neural networks. In this paper, we investigate novel and modified activation functions aimed at mitigating aliasing within neural amplifier models. Supporting this, we introduce a novel metric, the Aliasing-to-Signal Ratio (ASR), which quantitatively assesses the level of aliasing with high accuracy. Measuring also the conventional Error-to-Signal Ratio (ESR), we conducted studies on a range of preexisting and modern activation functions with varying stretch factors. Our findings confirmed that activation functions with smoother curves tend to achieve lower ASR values, indicating a noticeable reduction in aliasing. Notably, this improvement in aliasing reduction was achievable without a substantial increase in ESR, demonstrating the potential for high modeling accuracy with reduced aliasing in neural amp models.