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

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.

Automated summary

Music composition is challenging to automate due to the subjective nature of what is considered aesthetically pleasing. Past neural network-based methods have lacked consistency and failed to produce impressive results. In this project, we built upon Magenta's RL Tuner model and extended it to emulate the Galician Xota genre. By implementing a new rule-set and training a Deep Q Network using reward functions, we effectively enforced the desired style and structure on the generated compositions. Our research methodology provides a solid foundation for future studies using this architecture, and we propose further applications and improvements for this model in future work.