Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019 A GENERAL-PURPOSE DEEP LEARNING APPROACH TO MODEL TIME-VARYING AUDIO EFFECTS Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss Centre for Digital Music, Queen Mary University of London London, United Kingdom m.a.martinezramirez, emmanouil.benetos,
[email protected] ABSTRACT center frequency is modulated by an LFO or an envelope follower, the effect is commonly called auto-wah. Audio processors whose parameters are modified periodically Delay-line based audio effects, as in the case of flanger and over time are often referred as time-varying or modulation based chorus, are based on the modulation of the length of the delay audio effects. Most existing methods for modeling these type of lines. A flanger is implemented via a modulated comb filter whose effect units are often optimized to a very specific circuit and cannot output is mixed with the input audio. Unlike the phaser, the notch be efficiently generalized to other time-varying effects. Based on and peak frequencies caused by the flanger’s sweep comb filter convolutional and recurrent neural networks, we propose a deep effect are equally spaced in the spectrum, thus causing the known learning architecture for generic black-box modeling of audio pro- metallic sound associated with this effect. A chorus occurs when cessors with long-term memory. We explore the capabilities of mixing the input audio with delayed and pitch modulated copies deep neural networks to learn such long temporal dependencies of the original signal.