Berker Banar

Profile

Project: 

Towards Composing Contemporary Classical Music using Generative Deep Learning

Abstract:

In this project, our main goal is to design a generative deep learning system, which can compose contemporary classical music. Composition approach in this study is comprised of two main subparts, namely generating symbolic music and transferring symbolic music into the sonic/audio domain. Interpretable continuous control over musical attributes is one of the goals here as it is an open problem in the field and improves the functionality of such systems. We challenge the idea of learning for perfection, which is a typical approach in machine learning, and offer unusual-yet-compelling musical material aligned with contemporary classical music in the hope of expanding the current aesthetics of music and contributing to the musical culture with fresh and innovative compositional ideas. Primarily, this system is designed as a co-creative composition tool. Secondarily, there might be some other potential use cases such as robotic musicianship applications in the future.

C4DM theme affiliation:

Music Informatics

Research