RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2002.03082v1
• Date: Sat, 8 Feb 2020 03:53:52 GMT
• Title: RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
• Authors: Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang
• Abstract summary: This paper presents a deep reinforcement learning algorithm for online accompaniment generation. The proposed algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part.
• Score: 69.20460466735852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Different from offline music generation and harmonization, online music accompaniment requires the algorithm to respond to human input and generate the machine counterpart in a sequential order. We cast this as a reinforcement learning problem, where the generation agent learns a policy to generate a musical note (action) based on previously generated context (state). The key of this algorithm is the well-functioning reward model. Instead of defining it using music composition rules, we learn this model from monophonic and polyphonic training data. This model considers the compatibility of the machine-generated note with both the machine-generated context and the human-generated context. Experiments show that this algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part. Subjective evaluations on preferences show that the proposed algorithm generates music pieces of higher quality than the baseline method.

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