Automated composition of Galician Xota—tuning RNN-based composers for specific musical styles using deep Q-learning

Author:

Mira Rodrigo1,Coutinho Eduardo2ORCID,Parada-Cabaleiro Emilia3,Schuller Björn W.14

Affiliation:

1. GLAM—Group on Language Audio & Music, Department of Computing, Imperial College London, London, United Kingdom

2. Applied Music Research Lab, Department of Music, University of Liverpool, Liverpool, United Kingdom

3. Department of Music Pedagogy, Nuremberg University of Music, Germany

4. ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing, Universität Augsburg, Augsburg, Germany

Abstract

Music composition is a complex field that is difficult to automate because the computational definition of what is good or aesthetically pleasing is vague and subjective. Many neural network-based methods have been applied in the past, but they lack consistency and in most cases, their outputs fail to impress. The most common issues include excessive repetition and a lack of style and structure, which are hallmarks of artificial compositions. In this project, we build on a model created by Magenta—the RL Tuner—extending it to emulate a specific musical genre—the Galician Xota. To do this, we design a new rule-set containing rules that the composition should follow to adhere to this style. We then implement them using reward functions, which are used to train the Deep Q Network that will be used to generate the pieces. After extensive experimentation, we achieve an implementation of our rule-set that effectively enforces each rule on the generated compositions, and outline a solid research methodology for future researchers looking to use this architecture. Finally, we propose some promising future work regarding further applications for this model and improvements to the experimental procedure.

Publisher

PeerJ

Subject

General Computer Science

Reference49 articles.

1. Swarm intelligence and weak artificial creativity;Al-Rifaie,2013

2. Creativity and artificial intelligence;Boden,1997

3. Jambot: music theory aware chord based generation of polyphonic music with lstms;Brunner;ArXiv,2017

4. Creating melodies with evolving recurrent neural networks;Chen,2001

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