Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins
Title | Matching Artificial Reverb Settings to Unknown Room Recordings: A Recommendation System for Reverb Plugins |
Publication Type | Conference Paper |
Year of Publication | 2012 |
Authors | Peters, N., Lei H., & Choi J. |
Other Numbers | 3319 |
Abstract | For creating artificial room impressions, numerous reverb plugins exist, and are often controllableby manyparameters. To efficiently create a desired room impression, the sound engineer must be familiarwith all the available reverb setting possibilities. Although plugins are usually equipped withmany factory presets for exploring available reverb options, it is a time-consuming learningprocess to find the ideal reverb settings to create the desired room impression, especially ifvarious reverberation plugins are available. For creating a desired room impression based on areference audio sample, we present a method to automatically determine the best matching reverbpreset across different reverb plugins. Our method uses a supervised machine-learning approach andcan dramatically reduce the time spent on the reverb selection process. |
Acknowledgment | This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship. |
URL | http://www.icsi.berkeley.edu/pubs/other/artificialreverb12.pdf |
Bibliographic Notes | Proceedings of the 133th Audio Engineering Society (AES) Convention, San Francisco, California |
Abbreviated Authors | N. Peters, H. Lei, and J. Choi |
ICSI Research Group | Audio and Multimedia |
ICSI Publication Type | Article in conference proceedings |