Intelligent Multitrack Reverberation Based on Hinge-Loss Markov Random Fields

Benito AL, Reiss JD. Intelligent Multitrack Reverberation Based on Hinge-Loss Markov Random Fields. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017. Paper P1-3. Available from: https://aes.org/publications/elibrary-page/?id=18766

@inproceedings{Benito2017_18766,
  author = {Benito, Adán L. and Reiss, Joshua D.},
  title = {{Intelligent Multitrack Reverberation Based on Hinge-Loss Markov Random Fields}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  note = {Paper P1-3},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18766}
}

TY  - CPAPER
TI  - Intelligent Multitrack Reverberation Based on Hinge-Loss Markov Random Fields
AU  - Benito, Adán L.
AU  - Reiss, Joshua D.
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
M1  - Paper P1-3
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18766
PB  - Audio Engineering Society
LA  - en
AB  - We propose a machine learning approach based on hinge-loss Markov random fields to solve the problem of applying reverb automatically to a multitrack session. With the objective of obtaining perceptually meaningful results, a set of Probabilistic Soft Logic (PSL) rules has been defined based on best practices recommended by experts. These rules have been weighted according to the level of confidence associated with the mentioned practices based on existent evidence. The resulting model has been used to extract parameters for a series of reverb units applied over the different tracks to obtain a reverberated mix of the session.
ER  -