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
Subject:
Recording and Production
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can
subscribe to this RSS feed.
Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.