Identifying Saxophonists from Their Playing Styles

Maestre E, Pertusa A, Ramirez R. Identifying Saxophonists from Their Playing Styles. In: AES Conference: 30th International Conference: Intelligent Audio Environments. Audio Engineering Society; 2007. Paper 5. Available from: https://aes.org/publications/elibrary-page/?id=13939

@inproceedings{Maestre2007_13939,
  author = {Maestre, Esteban and Pertusa, Antonio and Ramirez, Rafael},
  title = {{Identifying Saxophonists from Their Playing Styles}},
  booktitle = {AES Conference: 30th International Conference: Intelligent Audio Environments},
  note = {Paper 5},
  year = {2007},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=13939}
}

TY  - CPAPER
TI  - Identifying Saxophonists from Their Playing Styles
AU  - Maestre, Esteban
AU  - Pertusa, Antonio
AU  - Ramirez, Rafael
T2  - AES Conference: 30th International Conference: Intelligent Audio Environments
M1  - Paper 5
PY  - 2007
DA  - 2007/03/06
UR  - https://aes.org/publications/elibrary-page/?id=13939
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes a machine learning approach to the problem of identifying professional musicians from their playing style. We focus on the identification of jazz saxophonists by studying how they express and communicate their view of the musical and emotional content of musical pieces (performed from a musical score). In particular, we investigate expressive deviations of parameters such as pitch, timing, amplitude and timbre in monophonic audio recordings. We describe how we extract a symbolic description from the audio recordings and how we use this symbolic description to train a performance-based interpreter classifier.
ER  -