Classical music prediction and composition by means of variational autoencoders


Abstract:

This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In thiswork, VAEs are used in a novelway to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen data.

Año de publicación:

2020

Keywords:

  • Variational autoencoders
  • Music composition
  • deep learning

Fuente:

googlegoogle
scopusscopus

Tipo de documento:

Article

Estado:

Acceso abierto

Áreas de conocimiento:

  • Aprendizaje automático
  • Ciencias de la computación

Áreas temáticas de Dewey:

  • Instrumentos y conjuntos instrumentales
  • Métodos informáticos especiales
  • Funcionamiento de bibliotecas y archivos
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 9: Industria, innovación e infraestructura
  • ODS 17: Alianzas para lograr los objetivos
  • ODS 4: Educación de calidad
Procesado con IAProcesado con IA