As an aid for musical analysis, in computational musicology mathematical and informatics tools have been developed to characterise quantitatively some aspects of musical compositions. A musical composition can be attributed by ear a certain amount of memory. These results are associated with repetitions and similarities of the patterns in musical scores. To higher variations, a lower amount of memory is perceived. However, the musical memory of a score has never been quantitatively defined. Here we aim to give such a measure following an approach similar to that used in physics to quantify the memory (non-Markovianity) of open quantum systems. We apply this measure to some existing musical compositions, showingthat the results obtained via this quantifier agree with what one expects by ear.The musical non-Markovianity quantifier can thus be used as a new tool that can aid quantitative musical analysis. It can also lead to future quantum computing controllers to manipulate structures in the framework of generative music.
Mannone Maria, Compagno Giuseppe (2022). Characterisation of the degree of musical non-Markovianity. JOURNAL OF CREATIVE MUSIC SYSTEMS, 6(1) [10.5920/jcms.975].
Characterisation of the degree of musical non-Markovianity
Mannone Maria
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2022-11-12
Abstract
As an aid for musical analysis, in computational musicology mathematical and informatics tools have been developed to characterise quantitatively some aspects of musical compositions. A musical composition can be attributed by ear a certain amount of memory. These results are associated with repetitions and similarities of the patterns in musical scores. To higher variations, a lower amount of memory is perceived. However, the musical memory of a score has never been quantitatively defined. Here we aim to give such a measure following an approach similar to that used in physics to quantify the memory (non-Markovianity) of open quantum systems. We apply this measure to some existing musical compositions, showingthat the results obtained via this quantifier agree with what one expects by ear.The musical non-Markovianity quantifier can thus be used as a new tool that can aid quantitative musical analysis. It can also lead to future quantum computing controllers to manipulate structures in the framework of generative music.File | Dimensione | Formato | |
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