DDSP Guitar Amp: Interpretable Guitar Amplifier Modeling

TL;DR

This paper introduces DDSP Guitar Amp, a differentiable digital signal processing model for guitar amplifier emulation that offers comparable performance to black-box models but with significantly reduced computational cost and improved interpretability.

Contribution

The paper presents a novel DDSP-based guitar amplifier model that incorporates physical design principles, achieving efficient and interpretable emulation of amplifier components.

Findings

Achieves comparable performance to black-box models.

Requires less than 10% of the computational operations per sample.

Demonstrates effectiveness using time- and frequency-domain metrics.

Abstract

Neural network models for guitar amplifier emulation, while being effective, often demand high computational cost and lack interpretability. Drawing ideas from physical amplifier design, this paper aims to address these issues with a new differentiable digital signal processing (DDSP)-based model, called ``DDSP guitar amp,'' that models the four components of a guitar amp (i.e., preamp, tone stack, power amp, and output transformer) using specific DSP-inspired designs. With a set of time- and frequency-domain metrics, we demonstrate that DDSP guitar amp achieves performance comparable with that of black-box baselines while requiring less than 10\% of the computational operations per audio sample, thereby holding greater potential for usages in real-time applications.