A Machine-Learning Approach to Application of Intelligent Artificial Reverberation
Journal of the Audio Engineering Society
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We propose a design of an adaptive digital audio effect for artificial reverberation, controlled directly by desired reverberation characteristics, that allows it to learn from the user in a super- vised way. The user provides monophonic examples of desired reverberation characteristics for individual tracks taken from the Open Multitrack Testbed. We use this data to train a set of models to automatically apply reverberation to similar tracks. We evaluate those models using classifier f1-scores, mean squared errors, and multi-stimulus listening tests.