Transcrição automática de sons polifônicos de guitarra na notação de tablaturas utilizando classificação temporal conexionista

• Ano de defesa: 2024
• Autor(a) principal: Gris, Lucas Rafael Stefanel
• Orientador(a): Soares, Anderson da Silva
• Banca de defesa: Soares, Anderson da Silva, Laureano, Gustavo Teodoro, Barbosa, Yuri de Almeida Malheiros
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RMG)
• País: Brasil
• Palavras-chave em Português: Transcrição musical; Transcrição automática de guitarra; Recuperação da informação musical
• Palavras-chave em Inglês: Automatic music transcription; Automatic guitar transcription; Music information retrieval
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/13749

Automatic Guitar Transcription, a branch of Automatic Musical Transcription, is a task with great applicability for musicians of fretted instruments such as the electric guitar and acoustic guitar. Often, musicians on these instruments transcribe or read songs and musical pieces in tablature format, a notation widely used for this type of instrument. Despite its relevance, this annotation is still done manually, making it a very complex process, even for experienced musicians. In this context, this work proposes the use of artificial intelligence to develop models capable of performing the task of transcribing polyphonic guitar sounds automatically. In particular, this work investigates the use of a specific method called Connectionist Temporal Classification (CTC), an algorithm that can be used to train sequence classification models without the need for alignment, a fundamental aspect for training more robust models, as there are few openly available datasets. Additionally, this work investigates multi-task learning for note prediction alongside tablature prediction, achieving significant improvements over conventional learning. Overall, the results indicate that the use of CTC is very promising for tablature transcription, showing only a 14.28% relative decrease compared to the result obtained with aligned data.