Intelligent Multitrack Reverberation Based on Hinge-Loss Markov Random Fields

摘要

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.