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Deep gradient reinforcement learning for music improvisation in cloud computing framework.

Fadwa Alrowais1, Munya A Arasi2, Saud S Alotaibi3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces artificial intelligence (AI) using reinforcement learning (RL) for real-time music improvisation. The AI model generates harmonically cohesive and aesthetically intriguing musical pieces, outperforming existing methods.

Keywords:
Cloud frameworksContainerizationGated recurrent unitsMusic improvisationReinforcement learning

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Area of Science:

  • Music Technology
  • Artificial Intelligence
  • Computational Creativity

Background:

  • Real-time music improvisation presents challenges in creating dynamic and flexible compositions.
  • Artificial intelligence (AI) offers potential solutions for enhancing human creativity in music.
  • Reinforcement learning (RL) is explored as a method for developing interactive music creation systems.

Purpose of the Study:

  • To explore the use of reinforcement learning (RL) techniques for creating interactive and responsive music improvisation systems.
  • To develop an AI agent capable of navigating musical possibilities for real-time improvisation.
  • To generate aesthetically intriguing and harmonically cohesive musical improvisations.

Main Methods:

  • Utilized bi-directional gated recurrent units to identify melodic frameworks in musical data.
  • Transformed musical elements (notes, chords, rhythms) into a format suitable for RL input.
  • Employed a deep gradient-based reinforcement learning technique with a custom reward system.
  • Trained the RL agent on the Bach Chorales dataset within a containerized cloud environment.
  • Rendered improvised music in MIDI format.

Main Results:

  • The proposed AI model achieved specific performance metrics: +0.15 for Pitch Frequency (PF), -0.43 for Standard Pitch Delay (SPD), -0.07 for Average Distance Between Peaks (ADP), and 0.0041 for Note Duration Gradient (NDG).
  • These results indicate superior performance compared to other music improvisation methods.
  • The model demonstrated the ability to generate harmonically cohesive and aesthetically intriguing improvisations.

Conclusions:

  • Reinforcement learning provides a viable approach for developing sophisticated AI-powered music improvisation systems.
  • The integration of deep learning techniques with RL enables the creation of novel and high-quality musical compositions.
  • The proposed method shows promise for advancing the field of computational creativity in music.