Open AccessAlgorithmic Modeling of Arts and Other Hard-to-Formalize Subjects
Author(s) -
Irina B. Gorbunova,
Sergey Chibirev
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7722.038620
Subject(s) - creativity , computer science , computational creativity , structuring , process (computing) , the arts , globe , construct (python library) , subject (documents) , musical , computer music , data science , perception , epistemology , psychology , visual arts , world wide web , art , social psychology , philosophy , finance , neuroscience , economics , programming language , operating system
This article is devoted to open questions related to modelling and using of modelling in various areas that hardly can be formalized, such as arts, creativity process, and some hard cases in natural science. We are discussing here the advantage of using modelling and the global question: why we need modelling in such areas and why. Several music and scientific centres across the globe are studying the ways of modelling the logical laws of creativity, by exploring generalized parameters of works of art including music. In recent years, Russian scientists have become more interested in modelling the process of musical creativity and music programming. Most frequently, these works are applied in the computer analysis of works of art, to determine the author or the period of creation, to attribute the work of art to a particular school or group. Fewer papers provide a deeper analysis, including psychological aspects of the perception of art (in particular, music). The authors have developed a method to construct models in subject areas difficult to formalize, applying it to create a model of musical creativity based on the structural analysis of musical texts, the cyclical structuring of statistical data, and the structural analysis of statistical information. This approach allows creating texts that satisfy the previously obtained or manually provided parameters.