Gradient-based Optimisation of Modulation Effects

2026-01-08

AI Summary

This work addresses the limitations of existing machine learning approaches for modeling guitar modulation effects—such as flanging, chorus, and phasing—which are often restricted to a single effect type or suffer from high latency, hindering real-time performance. The authors propose a unified modeling framework based on differentiable digital signal processing that enables zero-latency inference through time-domain execution while leveraging time–frequency domain training. A low-frequency-weighted loss function is introduced to mitigate local minima in learning delay parameters. The method supports efficient gradient-based optimization across multiple modulation effects, producing outputs for certain effects that are perceptually indistinguishable from those of real hardware. While significantly improving both generality and real-time capability, the approach still faces challenges with effects involving long delays or strong feedback structures.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modulation effects such as phasers, flangers and chorus effects are heavily used in conjunction with the electric guitar. Machine learning based emulation of analog modulation units has been investigated in recent years, but most methods have either been limited to one class of effect or suffer from a high computational cost or latency compared to canonical digital implementations. Here, we build on previous work and present a framework for modelling flanger, chorus and phaser effects based on differentiable digital signal processing. The model is trained in the time-frequency domain, but at inference operates in the time-domain, requiring zero latency. We investigate the challenges associated with gradient-based optimisation of such effects, and show that low-frequency weighting of loss functions avoids convergence to local minima when learning delay times. We show that when trained against analog effects units, sound output from the model is in some cases perceptually indistinguishable from the reference, but challenges still remain for effects with long delay times and feedback.

Problem

• modulation effects
• analog emulation
• computational cost
• latency
• delay time

Innovation

• differentiable DSP
• modulation effects
• zero-latency
• gradient-based optimization
• time-frequency training