Interactive Music Systems and AI
Title: Interactive Music Systems and AI
SNIC Project: Berzelius-2022-126
Project Type: LiU Berzelius
Principal Investigator: Kelsey Cotton <kelsey@chalmers.se>
Affiliation: Chalmers tekniska högskola
Duration: 2022-06-03 – 2023-01-01
Classification: 10209
Homepage: https://www.kelseycotton.com/
Keywords:

Abstract

Creative and artistic engagement with artificially intelligent systems is concurrent with the advancement of AI. The development and refinement of novel and advanced systems of music generation, analytical systems, curatorial practices, and collaborative performing agents is deeply entangled with artistic practices and culture. Through direct engagement with Research-through-Design and Research-through-Practice, we probe the aesthetic and ethical considerations of how one can (or cannot), should (or should not) creatively engage with interactive music systems and AI. This project is concerned with real time music applications of Machine Learning and Deep Learning, and human factors in AI and Machine Learning. Accordingly, this project will utilise Deep Learning and RAPIDS in the design and development of hard- and software systems, with a focus on human-machine collaboration. The intention is to afford nuanced, complex and iterative interaction with users within musical contexts. In so doing, we seek to formulate and reimagine new methodologies, ethical frameworks and language for AI interactive music and systems.