Adaptive Digital Audio Effects are sound transformations controlled by features extracted from the sound itself. Artificial reverberation is used by sound engineers in the mixing process for a variety of technical and artistic reasons, including to give the perception that it was captured in a closed space. We propose a design of an adaptive digital audio effect for artificial reverberation that allows it to learn from the user in a supervised way. We perform feature selection and dimensionality reduction on features extracted from our training data set. Then a user provides examples of reverberation parameters for the training data. Finally, we train a set of classifiers and compare them using 10-fold cross validation to compare classification success ratios and mean squared errors. Tracks from the Open Multitrack Testbed are used in order to train and test our models.

Authors: Chourdakis, Emmanouil Theofanis; Reiss, Joshua D.
Affiliation: Queen Mary University of London, London, UK
AES Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech) (January 2016)
Paper Number: 9-2
Publication Date: January 27, 2016 Import into BibTeX
Subject: Paper Session 9
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18090

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