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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.
Author (s): Vaneph, Alexandre; McNeil, Ellie; Rigaud, François; Silva, Rick
Affiliation:
Audionamix
(See document for exact affiliation information.)
AES Convention: 140
Paper Number:278
Publication Date:
2016-05-06
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Session subject:
eBriefs: Lectures
Permalink: https://aes2.org/publications/elibrary-page/?id=18182
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Vaneph, Alexandre; McNeil, Ellie; Rigaud, François; Silva, Rick; 2016; An Automated Source Separation Technology and Its Practical Applications [PDF]; Audionamix; Paper 278; Available from: https://aes2.org/publications/elibrary-page/?id=18182
Vaneph, Alexandre; McNeil, Ellie; Rigaud, François; Silva, Rick; An Automated Source Separation Technology and Its Practical Applications [PDF]; Audionamix; Paper 278; 2016 Available: https://aes2.org/publications/elibrary-page/?id=18182