SyVMO: Synchronous Variable Markov Oracle for Modeling and Predicting Multi-part Musical Structures

Autor:	Nádia Carvalho, Gilberto Bernardes
Rok vydání:	2021
Předmět:	Melody Structure (mathematical logic) Markov chain Computer science business.industry computer.software_genre Oracle Variable (computer science) Musical composition Polyphony Artificial intelligence Algorithmic composition business computer Natural language processing
Zdroj:	Artificial Intelligence in Music, Sound, Art and Design ISBN: 9783030729134 EvoMUSART
DOI:	10.1007/978-3-030-72914-1_3
Popis:	We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::17ea5bb55d844f05be6b05a4439d8c69 https://doi.org/10.1007/978-3-030-72914-1_3 Zobrazit plný text záznamu