利用基于学习的实时音乐伴奏进行人机合作钢琴演奏

Huijiang Wang, Xiaoping Zhang, Fumiya Iida
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引用次数: 0

摘要

机器学习领域的最新进展为音乐和娱乐机器人的开发铺平了道路。然而,人机合作弹奏乐器仍然是一项挑战,特别是由于复杂的运动协调和时间同步。在本文中,我们提出了基于非语言线索的人机合作钢琴演奏理论框架。首先,我们提出了一个音乐即兴演奏模型,该模型利用电流神经网络(RNN)根据人类的旋律输入预测适当的和弦行进。其次,我们提出了一种行为自适应控制器,以促进无缝的时间同步,使 cobot 能够产生和谐的音响效果。这种协作考虑到了人类与机器人之间的双向信息流。我们开发了一个基于熵的系统,通过分析人机协作过程中不同通信方式的影响来评估合作质量。同时,通过 MPC 自适应控制器,机器人可以在同音表演中与人类队友进行实时伴奏。我们所设计的框架已被验证能够有效地让人类和机器人在艺术钢琴演奏任务中协同工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human-Robot Cooperative Piano Playing with Learning-Based Real-Time Music Accompaniment
Recent advances in machine learning have paved the way for the development of musical and entertainment robots. However, human-robot cooperative instrument playing remains a challenge, particularly due to the intricate motor coordination and temporal synchronization. In this paper, we propose a theoretical framework for human-robot cooperative piano playing based on non-verbal cues. First, we present a music improvisation model that employs a recurrent neural network (RNN) to predict appropriate chord progressions based on the human's melodic input. Second, we propose a behavior-adaptive controller to facilitate seamless temporal synchronization, allowing the cobot to generate harmonious acoustics. The collaboration takes into account the bidirectional information flow between the human and robot. We have developed an entropy-based system to assess the quality of cooperation by analyzing the impact of different communication modalities during human-robot collaboration. Experiments demonstrate that our RNN-based improvisation can achieve a 93\% accuracy rate. Meanwhile, with the MPC adaptive controller, the robot could respond to the human teammate in homophony performances with real-time accompaniment. Our designed framework has been validated to be effective in allowing humans and robots to work collaboratively in the artistic piano-playing task.
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