Towards Machine Musicians Who Have Listened to More Music Than Us: Audio Database-led Algorithmic Criticism for Automatic Composition and Live Concert Systems

Collins, Nick

Abstract

Databases of audio can form the basis for new algorithmic critic systems, applying techniques from the growing field of music information retrieval to meta-creation in algorithmic composition and interactive music systems. In this article, case studies are described where critics are derived from larger audio corpora. In the first scenario, the target music is electronic art music, and two corpuses are used to train model parameters and then compared with each other and against further controls in assessing novel electronic music composed by a separate program. In the second scenario, a “real-world” application is described, where a “jury” of three deliberately and individually biased algorithmic music critics judged the winner of a dubstep remix competition. The third scenario is a live tool for automated in-concert criticism, based on the limited situation of comparing an improvising pianists' playing to that of Keith Jarrett; the technology overlaps that described in the other systems, though now deployed in real time. Alongside description and analysis of these systems, the wider possibilities and implications are discussed.

Citation

Collins, N. (2016). Towards Machine Musicians Who Have Listened to More Music Than Us: Audio Database-led Algorithmic Criticism for Automatic Composition and Live Concert Systems. Computers in entertainment, 14(3), Article 2. https://doi.org/10.1145/2967510

• Journal Article Type: Article
• Acceptance Date: Jun 27, 2016
• Online Publication Date: Dec 1, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: Sep 13, 2016
• Publicly Available Date: Oct 4, 2016
• Journal: Computers in entertainment CIE.
• Electronic ISSN: 1544-3574
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 3
• Article Number: 2
• DOI: https://doi.org/10.1145/2967510
• Public URL: https://durham-repository.worktribe.com/output/1376645