Neural networks synthesize controllable musical distortion tones in real time
Learned Continuous Synthesis of Quadratic Difference Tone Spectra
Sound
Summary
Sometimes, people hear extra tones called quadratic difference tones (QDTs) that aren't actually played but are created by the ear. These tones can make music sound richer and more interesting. The authors created a neural network that learns how to produce these tones more smoothly and continuously, overcoming problems in older methods that were harder to control and only worked on steady sounds. While this new method is slightly less precise in calculations, it sounds good enough for making music and runs in real time. They also made easy-to-use software so musicians can try it out.
What this means in practice
- •For electronic musicians: Use real-time neural synthesis of quadratic difference tones to create richer, more expressive sounds during live musical performances.
- •For game audio developers: Integrate continuous QDT synthesis into game audio engines to produce complex and dynamically controllable sound effects beyond static tones.
Authors
Esteban Gutiérrez, Behzad Haki, Christopher Haworth, Xavier Serra, Rodrigo Cádiz
Abstract
Quadratic difference tones (QDTs) are a species of auditory distortion product in which a "phantom" pure tone, absent from the acoustic signal, is clearly audible to listeners. Exploiting this phenomenon, one can synthesize harmonically rich tones for musical purposes, a technique called Quadratic Difference Tone Spectrum (QDTS) synthesis. Previous works have introduced numerical methods to synthesize QDTS based on the distortion function, which links a target QDTS and an overtone-structured carrier signal. While accurate, these methods were stochastic and discontinuous, making them difficult to control for musical purposes and effectively limiting them to stationary signals. This paper proposes a neural network-based approach that learns an approximate inverse of the distortion mapping in an autoencoder-like configuration, producing a continuous approximation that addresses prior limitations. Experimental results show that, although slightly less numerically precise, the method is sufficient for perceptual and musical applications. We also implement a real-time version in Max and evaluate its performance. Various sound examples demonstrate its expressive and musical potential. The source code, audio examples, tutorials, and software accompanying this work are available at https://cordutie.github.io/projects/qdts.html