Automatic Control of a Digital Reverberation Effect using Hybrid Models

Chourdakis ET, Reiss JD. Automatic Control of a Digital Reverberation Effect using Hybrid Models. In: AES Conference: 60th International Conference: Dereverberation and Reverberation of Audio, Music, and Speech. Audio Engineering Society; 2016. Paper 9-2. Available from: https://aes.org/publications/elibrary-page/?id=18090

@inproceedings{Chourdakis2016_18090,
  author = {Chourdakis, Emmanouil Theofanis and Reiss, Joshua D.},
  title = {{Automatic Control of a Digital Reverberation Effect using Hybrid Models}},
  booktitle = {AES Conference: 60th International Conference: Dereverberation and Reverberation of Audio, Music, and Speech},
  note = {Paper 9-2},
  year = {2016},
  month = jan,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18090}
}

TY  - CPAPER
TI  - Automatic Control of a Digital Reverberation Effect using Hybrid Models
AU  - Chourdakis, Emmanouil Theofanis
AU  - Reiss, Joshua D.
T2  - AES Conference: 60th International Conference: Dereverberation and Reverberation of Audio, Music, and Speech
M1  - Paper 9-2
PY  - 2016
DA  - 2016/01/06
UR  - https://aes.org/publications/elibrary-page/?id=18090
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -