Audio source separation, the process of un-mixing, has long been seen as unreachable, "the holy grail." Recent progress in coupling digital signal processing with machine deep learning puts this process within reach of the typical sound audio engineer. Using our technology, we will demonstrate a few examples of separations focused on isolating voice tracks from fully arranged mixes and the opportunities that can be realized from this technology in a series of industry case studies.
Authors:
Vaneph, Alexandre; McNeil, Ellie; Rigaud, François; Silva, Rick
Affiliation:
Audionamix
AES Convention:
140 (May 2016)
eBrief:278
Publication Date:
May 26, 2016
Subject:
eBriefs: Lectures
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.