Gerando letras musicais utilizado uma rede neural recorrente LSTM - Long Short-Term Memory.

• Autor(a) principal: Juliani, Jeferson da Silva
• Data de Publicação: 2019
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/49519
• topic: LSTM (Long Short-Term Memory); Geração de letras musicais.; Aprendizagem profunda; Rede neural recorrente

The use of machine learning has been used for various purposes, including the generation of various types of art, such as visual, sound or textual. The use of these learning algorithms makes it possible to develop natural language generation applications. Problems of natural language generation mostly use recurrent neural networks, a well-researched subject, with much proof of concepts and variations. However, a more specific application for this problem is song lyrics. In this work, a method was presented and the results of the automatic generation of lyrics using a Long Short-Term Memory (LSTM) network were discussed. For the creation of the datasets a crawler developed for this work was used, that captured 562 songs of 3 artists of different styles. The experiments were performed separately with each style. The results of the experiments showed a different grammatical structure with the RAP corpus, which began to rhyme in some of the generated sentences. It was also possible to observe the generation of some acceptable musical sentences.