论文标题
RL-DUET：使用深厚的增强学习的在线音乐伴奏
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
论文作者
论文摘要
本文介绍了一种深入的增强学习算法，用于在线伴奏，并具有实时交互式人机二重奏即兴创作的潜力。与离线音乐的生成和协调不同，在线音乐伴奏需要算法对人的输入做出响应，并以顺序生成机器对应物。我们将其视为一个加强学习问题，在该问题中，一代代理商学习了基于先前生成的上下文（状态）生成音符（动作）的策略。该算法的关键是功能齐全的奖励模型。我们没有使用音乐构图规则来定义它，而是从单声音和多形培训数据中学习了该模型。该模型考虑了机器生成的音符与机器生成的上下文和人类生成的环境的兼容性。实验表明，该算法能够响应人类部分并产生旋律，谐波和多样化的机器部分。对偏好的主观评估表明，所提出的算法比基线方法生成更高质量的音乐作品。
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.
