Open AccessCharacterization and identification of twelve-tone composers
Author(s) -
Lucas Francesco Piccioni Costa,
Eugen Coca
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2018.4465
Subject(s) - tone (literature) , identification (biology) , musical , relation (database) , categorization , speech recognition , signature (topology) , computer science , personality , artificial intelligence , art , psychology , literature , mathematics , data mining , social psychology , botany , geometry , biology
The individualism of each composer is shaped in an inherent way to his personality, aiming for recognition of particular form through the own songs. In this way, it is possible to categorize a musical subgenre at a deeper level by identifying the composer from his works. However, the characteristics of each composer are so varied that they are difficult to identify. In this paper it is proposed to use machine learning to classify works of twelve-tone music according to the composer, under the hypothesis that in choosing the twelve-tone series a part of his signature was reflected. Experimental results showed promising performance and confirmed the existence of a relation between composer and series.