Artificial intelligence applied to musical genres analysis

Author(s)

Débora Cristina Corrêa

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Física de São Carlos (IFSC/BT)
• Defense date: 2012-12-05

Examining board members

Luciano da Fontoura Costa; Alessandro Lameiras Koerich; José Eduardo Fornari Novo Junior; Jose Hiroki Saito; Agma Juci Machado Traina

• Advisor: Luciano da Fontoura Costa

Abstract

Musical databases have increased in number and size continuously, paving the way to large amounts of online music data, including discographies, biographies and lyrics. The constant growth of data on the Internet has attracted musical research for developing tools to analyze and classify music data. The main objective of such tools is to extract reliable information to adequately represent and compact music content in databases. In this context, musical genres are particularly interesting descriptors, since they have being used for years to organize music collections, reflect interaction between cultures and summarize common features (or patterns) between musical pieces. The main motivation of this study is to propose a original and low cost framework to represent musical genres, as well as investigate the contribution of this representation in applications and studies that are placed in the context of music information retrieval researches. The representation of music content is referred to the rhythmic patterns, since rhythm configures a significant aspect in the discrimination of musical genres. The rhythmic patterns are determined by the temporal dependency of the musical notes present in the percussion, so that each song is represented by a vector of conditional probabilities between pairs and triples of notes, computed by the use of first and second order Markov chains. The rhythm patterns from distinct genres are investigated in applications such as: classification, music synthesis, music recommendation, mood/emotion in music, and analysis of evolutionary aspects. The main finding is that the rhythmic patterns as established in this study are sensitive to the genre discrimination, suggesting that there are sequences of notes common to all genres, and sequences that are distinct and characteristics of each one. A second motivation for this study is the use of topological measures of music networks and music digraphs for the data analysis. Communities obtained from these networks contributed to the definition of an unsupervised approach that provided performance rates superior to the hierarchical clustering. The rhythmic patterns also motivated the development of strategies for automatic composition, for the generation of playlists, and the analysis of the relationship between these patterns and emotional aspects. Finally, a statistical analysis of the rhythm evolution is performed, in which the principal finding is the presence of innovation and retrieval mechanisms for all genres. These mechanisms seems to be the result of the competition between factors that promote the innovation, and factors that prevent it, as, for example, the obedience to composition rules that retains the fundamental characteristics of each genre. (AU)