In this paper a knowledge-engineered mixing engine is introduced that uses semantic mixing rules and bases mixing decisions on instrument tags as well as elementary, low-level signal features. Mixing rules are derived from practical mixing engineering textbooks. The performance of the system is compared to existing automatic mixing tools as well as human engineers by means of a listening test, and future directions are established.

Authors: De Man, Brecht; Reiss, Joshua D.
Affiliation: Queen Mary University of London, London, UK
AES Convention: 135 (October 2013) Paper Number: 8961
Publication Date: October 16, 2013 Import into BibTeX
Subject: Recording and Production
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=17011

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 135Papers)   /conv/135/8961.pdf

Start a discussion about this paper!