A TOOL FOR GENERATING AND EXPLAINING EXPRESSIVE MUSIC PERFORMANCES OF MONOPHONIC JAZZ MELODIES

Autor:	Amaury Hazan, Rafael Ramirez
Rok vydání:	2006
Předmět:	Melody MIDI business.industry Transcription (music) Computer science Speech recognition computer.file_format computer.software_genre Artificial Intelligence Artificial intelligence business Jazz computer Natural language processing
Zdroj:	International Journal on Artificial Intelligence Tools. 15:673-691
ISSN:	1793-6349 0218-2130
DOI:	10.1142/s0218213006002862
Popis:	In this paper we present a machine learning approach to modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance of a piece. We describe a tool for both generating and explaining expressive music performances of monophonic Jazz melodies. The tool 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 induce both an expressive transformation model and a set of expressive performance rules from the 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. We compare several machine learning techniques we have explored for inducing the expressive transformation model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::53e6a75e8ecb818acaaa645d22807161 https://doi.org/10.1142/s0218213006002862 Zobrazit plný text záznamu