Synthesis of Emotionally Expressive Music Performance Using Machine Learning

Research Overview

This project is associated with three laboratories from KAIST and Seoul National University, and Samsung Cooperation.

This project challenges the question whether a machine can perform music in an emotionally expressive manner with a specific style, and mainly aims to develop a novel human-competitive system that generates music performance conveying an emotion and a style. The proposed system is composed of multiple neural network modules to acquire, analyze and synthesize music performance data.

Figure 1. A diagram of overall system

In order to successfully build the overall system, we divide the main goal into several subtasks; in MARG, we focus on defining useful features on analyzing emotion from acquired performance data as well as building a system that quantitatively evaluates music performance using machine learning techniques.     

The outcomes from the project would provide a number of useful insights on how to represent the elements of music performance in a manner that computer can understand, and modeling the artistic process with various types of neural networks.