Comparative Study Of State-based Neural Networks For Virtual Analog Audio Effects Modeling

Abstract

Artificial neural networks are a promising technique for virtual analog modeling, having shown particular success in emulating distortion circuits. Despite their potential, enhancements are needed to enable effect parameters to influence the network's response and to achieve a low-latency output. While hybrid solutions, which incorporate both analytical and black-box techniques, offer certain advantages, black-box approaches, such as neural networks, can be preferable in contexts where rapid deployment, simplicity, or adaptability are required, and where understanding the internal mechanisms of the system is less critical. In this article, we explore the application of recent machine learning advancements for virtual analog modeling. We compare State-Space models and Linear Recurrent Units against the more common LSTM networks, with a variety of audio effects. We evaluate the performance and limitations of these models using multiple metrics, providing insights for future research and

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