Robotic Musicianship Based on Least Squares and Sequence Generative Adversarial Networks

Abstract

Robotic musicianship research aims to the configuration of robots can analyze, reason, and generate music autonomous. The goal of this research is to achieve the inspiring and meaningful musical interactions between humans and artificially creative robots. This research presents a new model of automatic music generation which is based on least squares and generative adversarial networks (GANs). This research specifies classical piano as the music source and uses the sensors as the context-awareness technology to sense and receive the input audio in human-robot interaction. Therefore, this research applies sequence generation adversarial network (SeqGAN) techniques that are better able to address discrete issues in generating samples of classical piano melodies and datasets. Modifying the SeqGAN approach, this research presented the Least Squares SeqGAN (LS-SeqGAN) method to create melody units on different chords and generates a set of music pieces as testing dataset. In this research, we implement the original method and use the least squares method to stabilize the training of GANs. The performance evaluation shows that proposed LS-SeqGAN method can fulfill the need both of music quality and creativity. It offers a robust infrastructure for the human-robotic interaction that can be used to promote the related robotic applications.