A Learning Scheme for Generating Expressive Music Performances of Jazz Standards
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1628-1628.
Abstract
We describe our approach for generating expressive music performances of monophonic Jazz melodies. It consists of three components: (a) a melodic transcription component which extracts a set of acoustic features from monophonic recordings, (b) a machine learning component which induces an expressive transformation model from the set of extracted acoustic features, and (c) a melody synthesis component which generates expressive monophonic output (MIDI or audio) from inexpressive melody descriptions using the induced expressive transformation model. In this paper we concentrate on the machine learning component, in particular, on the learning scheme we use for generating expressive audio from a score.
@inproceedings{ijcai2005p324,
title = {A Learning Scheme for Generating Expressive Music Performances of Jazz Standards},
author = {Ramírez, Rafael and Hazan, Amaury},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1628--1628},
year = {2005},
url = {https://ijcai.org/proceedings/2005/324},
}
TY - CPAPER TI - A Learning Scheme for Generating Expressive Music Performances of Jazz Standards AU - Ramírez, Rafael AU - Hazan, Amaury T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1628 EP - 1628 UR - https://ijcai.org/proceedings/2005/324 ER -Copied
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