Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins

Peters N, Choi J, Lei H. Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins. In: AES Convention 133. Audio Engineering Society; 2012. Paper 8700. Available from: https://aes.org/publications/elibrary-page/?id=16443

@inproceedings{Peters2012_16443,
  author = {Peters, Nils and Choi, Jaeyoung and Lei, Howard},
  title = {{Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins}},
  booktitle = {AES Convention 133},
  note = {Paper 8700},
  year = {2012},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16443}
}

TY  - CPAPER
TI  - Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins
AU  - Peters, Nils
AU  - Choi, Jaeyoung
AU  - Lei, Howard
T2  - AES Convention 133
M1  - Paper 8700
PY  - 2012
DA  - 2012/10/06
UR  - https://aes.org/publications/elibrary-page/?id=16443
PB  - Audio Engineering Society
LA  - en
AB  - For creating artificial room impressions, numerous reverb plugins exist and are often controllable by many parameters. To efficiently create a desired room impression, the sound engineer must be familiar with all the available reverb setting possibilities. Although plugins are usually equipped with many factory presets for exploring available reverb options, it is a time-consuming learning process to find the ideal reverb settings to create the desired room impression, especially if various reverberation plugins are available. For creating a desired room impression based on a reference audio sample, we present a method to automatically determine the best matching reverb preset across different reverb plugins. Our method uses a supervised machine-learning approach and can dramatically reduce the time spent on the reverb selection process.
ER  -