CONMOD: Controllable Neural Frame-based Modulation Effects

TL;DR

CONMOD is a neural model that emulates LFO-driven audio effects like phaser and flanger, allowing control over parameters and effect interpolation for creative sound design.

Contribution

It introduces a controllable, universal neural framework for LFO-driven effects, enabling parameter manipulation and effect blending in a single model.

Findings

Outperforms previous models in effect emulation quality.

Provides controllability over LFO frequency and feedback.

Enables interpolation between different effects.

Abstract

Deep learning models have seen widespread use in modelling LFO-driven audio effects, such as phaser and flanger. Although existing neural architectures exhibit high-quality emulation of individual effects, they do not possess the capability to manipulate the output via control parameters. To address this issue, we introduce Controllable Neural Frame-based Modulation Effects (CONMOD), a single black-box model which emulates various LFO-driven effects in a frame-wise manner, offering control over LFO frequency and feedback parameters. Additionally, the model is capable of learning the continuous embedding space of two distinct phaser effects, enabling us to steer between effects and achieve creative outputs. Our model outperforms previous work while possessing both controllability and universality, presenting opportunities to enhance creativity in modern LFO-driven audio effects.