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Analysis Project - Análise De Música Simbólica Por Padrões

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Analysis Project - Análise De Música Simbólica Por Padrões UNIVERSIDADE DE SÃO PAULO ECA - ESCOLA DE COMUNICAÇÃO E ARTES. DEPARTAMENTO DE MÚSICA ROBSON DIAS ANALYSIS PROJECT - ANÁLISE DE MÚSICA SIMBÓLICA POR PADRÕES. (Versão Corrigida (versão original disponível na Biblioteca da ECA/USP)) TESE DE DOUTORADO SÃO PAULO 2020 2 ROBSON DIAS ANALYSIS PROJECT - ANÁLISE DE MÚSICA SIMBÓLICA POR PADRÕES. (Versão Corrigida (versão original disponível na Biblioteca da ECA/USP)) Tese de doutorado apresentado ao Departamento de Música da Escola de Comunicação e Artes da Universidade de São Paulo como parte dos requisitos necessários à obtenção do título de doutor em musicologia. Orientador: Diósnio Machado Neto SÃO PAULO 2020 Autorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meio convencional ou eletrônico, para fins de estudo e pesquisa, desde que citada a fonte. Catalogação na Publicação Serviço de Biblioteca e Documentação Escola de Comunicações e Artes da Universidade de São Paulo Dados inseridos pelo(a) autor(a) __________________________________________________________________________ DIAS, ROBSON Analysis Project - Análise de Música Simbólica por Padrões / ROBSON DIAS; orientador, Diósnio Machado Neto. -- São Paulo, 2020. 258 p.: il. Tese (Doutorado) - Programa de Pós-Graduação em Música Escola de Comunicações e Artes / Universidade de São Paulo. Bibliografia Versão corrigida 1. Análise Musical Computacional 2. Música Simbólica 3. Padrões e Esquemas Musicais 4. Figuras de Retórica 5. Programação em Python I. Machado Neto, Diósnio II. Título. CDD 21.ed. - 780 ______________________________________________________________________ Elaborado por Alessandra Vieira Candole Maldonado - CRB-8/6194 4 FOLHA DE APROVAÇÃO DIAS, Robson. Analysis Project - Análise de Música Simbólica por Padrões. 237 p. Tese (Doutorado) - Programa de Pós-Graduação em Música Escola de Comunicações e Artes / Universidade de São Paulo, 2020. Aprovado em: 19/06/2020 Professor (a) Doutor (a): Diósnio Machado Neto_____________________________ Instituição: EACH - Universidade de São Paulo (USP)_________________________ Julgamento:__________________________________________________________ Assinatura:__________________________________________________________ Professor (a) Doutor (a): Regis Rossi Alves Faria____________________________ Instituição: EACH – Universidade de São Paulo (USP)________________________ Julgamento:__________________________________________________________ Assinatura:__________________________________________________________ Professor (a) Doutor (a): Renato Fabbri____________________________________ Instituição: Externo____________________________________________________ Julgamento:__________________________________________________________ Assinatura:__________________________________________________________ Professor (a) Doutor (a): Anselmo Guerra de Almeida_________________________ Instituição: Universidade Federal de Goiás (UFGoiás) – Externo_________________ Julgamento:__________________________________________________________ Assinatura:__________________________________________________________ Professor (a) Doutor (a): Maria Cristina Ferreira de Oliveira____________________ Instituição: ICMC - Universidade de São Paulo (USP)_________________________ Julgamento:__________________________________________________________ Assinatura:_________________________________________________________ Agradecimentos Ao meu orientador que mesmo reconhecendo minhas limitações acreditou em mim e foi muito generoso ao me conduzir na difícil realização desta tarefa. A minha mãe, uma das figuras mais importantes na construção do meu caráter. Obrigado por me cobrar tanto. Theo lembre-se: You and me, together forever. Waniaceli Dias, companheira e amiga, obrigado por me acompanhar mesmo me minas “loucuras”. Ronaldo Novaes, sem você esta tese não teria acontecido. Obrigado por acolher em sua casa. Aquela “economia” com o hotel viabilizou meus estudos. Amigos e irmãos para sempre. Todos que direta ou indiretamente fazem parte da construção do que sou: agradeço a fidelidade e o companheirismo. 6 Quem não sabe o que procura não entende o que encontra“” (Hans Selye) RESUMO O presente trabalho realizou o estudo de música simbólica tendo o como objetivo a de busca padrões e esquemas pré-determinados em um grupo extenso de obras. Foram escolhidas algumas figuras retórico musicais e esquemas tratados na obra Music in The Galant Style de Gjerdingen(2007). Para a realização da pesquisa fez-se necessário o desenvolvimento de algoritmos de buscas bem como um método capaz de organizar as fases do trabalho analítico que foi realizado em um ambiente computacional. O Analysis Project (nome dado a ferramenta de suporte a este trabalho) realizou todo o processo. Verificamos se o quantitativo de repetições de determinado padrão encontradas em um conjunto de obras nos facilitaria a compreensão sobre os processos de estilo e recepção daquele contexto. Esta era nossa hipótese inicial. Os resultados nos mostraram que o processamento computacional inerentes ao modelo de representação do conteúdo de interesse possui limitações. Realizamos uma análise baseada na identificação de padrões sintáticos que facilmente poderiam ser reconhecidos no texto musical contudo estes padrões não capturam a semântica do texto. Palavras-chave: Musicologia; Análise musical computacional; Tecnologia; Python. 8 ABSTRACT The present work carried out the study of symbolic music with the aim of searching for pre-determined patterns and schemes in an extensive group of works. Some rhetorical musical figures and schemes dealt with in the work Music in The Galant Style by Gjerdingen (2007) were chosen. In order to carry out the research, it was necessary to develop search algorithms as well as a method capable of organizing the phases of the analytical work that was carried out in a computational environment. The Analysis Project (the name given to the support tool for this work) performed the entire process. We verified whether the number of repetitions of a given pattern found in a set of works would facilitate our understanding of the style and reception processes in that context. This was our initial hypothesis. The results showed that the computational processing inherent to the model of representation of the content of interest has limitations. We performed an analysis based on the identification of syntactic patterns that could easily be recognized in the musical text, however these patterns do not capture the semantics of the text. Keywords: Musicology; Computational musical analysis; Technology; Software, Python. LISTA DE ILUSTRAÇÕES Figura 1 – Analista X Obra Musical ........................................................................... 23 Figura 2 - Analista X Computador X obra .................................................................. 23 Figura 3 - Página Inicial do MelodicMatch ................................................................. 26 Figura 4 - Visualização do site oficial do Music21 ..................................................... 27 Figura 5 - Nota impressa ........................................................................................... 28 Figura 6 - Music21 - Pequena Melodia ...................................................................... 28 Figura 7 - Visualização do site oficial do ELVIS ........................................................ 29 Figura 8 - Visualização do site oficial do MusicXML Analyser ................................... 30 Figura 9 - – Frequências em um grupo de obras no formato MusicXML ................... 31 Figura 10 - Interface para inserir padrões para pesquisa nos arquivos MusicXML. .. 32 Figura 11 - O padrão pesquisado aparece em vermelho. ......................................... 33 Figura 12 - Modelo de Metodologia Analítica ............................................................ 37 Figura 13 - Metodologia fase 01 ................................................................................ 38 Figura 14 - Cadastro de Projetos .............................................................................. 38 Figura 15 - Cadastro de projetos - obras musicais .................................................... 39 Figura 16 - Lista das estâncias de um projeto ........................................................... 40 Figura 17 - Cadastro de instâncias de projetos ......................................................... 40 Figura 18 - Representação visual da primeira fase de pesquisa ............................... 41 Figura 19 - Metodologia fase 02 ................................................................................ 42 Figura 20 - Lista parcial dos módulos e associações com as instâncias ................... 43 Figura 21 - Cadastro de módulos .............................................................................. 43 Figura 22 - Cadastro de categorias ........................................................................... 44 Figura 23 - Categoria com a opção de padrões do Gjerdingen ................................. 45 Figura 24 - Cadastro de categorias com a opção de padrão do usuário ................... 46 Figura 25 - Cadastro de categorias com opção de padrão do usuário 02 ................. 46 Figura 26 - Representação visual da primeira e segunda fase de pesquisa ............. 47 Figura 27 - Metodologia fase 03 ................................................................................ 48 Figura 28 - Lista dos projetos a pesquisar ................................................................ 49 Figura 29
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