An automatic music production system based on expert audio engineering knowledge is proposed. An expert system based on a probabilistic graphical model is employed to embed professional audio engineering knowledge and infer automatic production decisions based on musical information extracted from audio files. The production pattern, which is represented as probabilistic graphical model, can be ‘learned’ from the operation data of a human audio engineer or manually constructed from domain knowledge. The authors also discuss the real-time implementation of the proposed automatic production system for live mixing application scenarios. Musical event alignment and prediction algorithms are introduced to improve the time synchronization performance of our production model. The authors conclude with performance evaluations and a brief summary.

Authors: Bocko, Gregory; Bocko, Mark F.; Headlam, Dave; Lundberg, Justin; Ren, Gang
Affiliations: Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA; Dept. of Music Theory, University of Rochester, Rochester, NY, USA(See document for exact affiliation information.)
AES Convention: 129 (November 2010) Paper Number: 8255
Publication Date: November 4, 2010 Import into BibTeX
Subject: Signal Analysis and Synthesis
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=15677

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 129Papers)   /129/8255.pdf

Start a discussion about this paper!