2:20 PM - 2:40 PM
[1H2-J-13-04] Music Composition based on the Genetic Programming with segmented VRAE
Keywords:music, automatic generation, Genetic Programming, Deep Learning
Automatic music composition is one of the most difficult and attractive challenges in the artificial intelligence (AI) field. In order to tackle this challenge, an approach using interactive evolutionary computation (IEC) is drawing attention because IEC takes human emotions into consideration.
We have proposed an automatic music composition system based on IEC with a surrogate model called an evaluation model.
In the previous study, the model is constructed with a Variational Recurrent Auto-Encoder (VRAE) to achieve quantitative evaluations.
However, it is not easy for a simple VRAE to map tunes' features into a meaningful latent space regardless of their lengths.
This paper focuses on the way to map tunes with different length into a good latent space and the application for IEC.
The evaluation model employs a hierarchical VRAE called segmented VRAE.
The experiments are carried out to show the effectiveness of the proposed method.
We have proposed an automatic music composition system based on IEC with a surrogate model called an evaluation model.
In the previous study, the model is constructed with a Variational Recurrent Auto-Encoder (VRAE) to achieve quantitative evaluations.
However, it is not easy for a simple VRAE to map tunes' features into a meaningful latent space regardless of their lengths.
This paper focuses on the way to map tunes with different length into a good latent space and the application for IEC.
The evaluation model employs a hierarchical VRAE called segmented VRAE.
The experiments are carried out to show the effectiveness of the proposed method.