Go witheFlow: Real-time Emotion Driven Audio Effects Modulation

Edmund Grigoris Dervakos, Spyridon Kantarelis, Vassilis Lyberatos, Jason Liartis, Giorgos Stamou

Track: Paper

Keywords: music performance, collaborative AI, affective computing, audio effects

TL;DR: witheFlow integrates biosignals with audio processing and AI to enhance musical performance while maintaining human creative agency.

Abstract: Music performance is a distinctly human activity, intrinsically linked to the performer’s ability to convey, evoke, or express emotion. Machines cannot perform music in the human sense, they can produce, reproduce, execute or synthesize music, but they lack the capacity for affective or emotional experience. As such, music performance is an ideal candidate through which to explore aspects of collaboration between humans and machines. In this paper, we introduce the witheFlow system, designed to enhance real-time music performance by automatically modulating audio effects based on features extracted from both biosignals and the audio itself. The system, currently in a proof-of-concept phase, is designed to be lightweight, able to run locally on a laptop, and is open-source given the availability of a compatible Digital Audio Workstation, and sensors.

Submission Number: 47