Music offers a uniquely abstract way for the expression of human emotions and moods, wherein melodic harmony is achieved through a succinct blend of pitch, rhythm, tempo, texture, and other sonic qualities. The emerging field of “Robotic Musicianship” focuses on developing machine intelligence, in terms of algorithms and cognitive models, to capture the underlying principles of musical perception, composition, and performance. The capability of new-generation robots to manifest music in a human-like artistically expressive manner lies at the intersection of engineering, computers, music, and psychology; promising to offer new forms of creativity, sharing, and interpreting musical impulses. This manuscript explores how real-time collaborations between humans and machines might be achieved by the integration of technological and mathematical models from Synchronization and Learning, with precise configuration for the seamless generation of melody in tandem, towards the vision of human–robot symphonic orchestra. To explicitly capture the key ingredients of a good symphony—synchronization and anticipation—this work discusses a possible approach based on the joint strategy of: (i) Mapping— wherein mathematical models for oscillator coupling like Kuramoto could be used for establishing and maintaining synchronization, and (ii) Modelling—employing modern deep learning predictive models like Neural Network architectures to anticipate (or predict) future state changes in the sequence of music generation and pre-empt transitions in the coupled oscillator sequence. It is hoped that this discussion will foster new insights and research for better “real-time synchronized human-computer collaborative interfaces and interactions”.

音乐为表达人类的情感和情绪提供了一种独特的抽象方式，其中旋律的和谐是通过音调，节奏，节奏，质感和其他声音品质的简洁融合而实现的。“机器人音乐学”的新兴领域”致力于根据算法和认知模型开发机器智能，以捕捉音乐感知，构图和演奏的基本原理。新一代机器人以类似人的艺术表现方式表现音乐的能力在于工程学，计算机，音乐和心理学的交汇处。承诺提供新形式的创造力，分享和诠释音乐冲动。该手稿探讨了如何通过将同步和学习技术与数学模型相集成，实现精确的配置以无缝地串联产生旋律，实现人机交响乐团的构想，从而实现人机之间的实时协作。。为了明确把握好交响乐的关键要素（同步和预期），这项工作讨论了一种基于以下联合策略的可行方法：（i）映射-其中可使用振荡器耦合的数学模型（例如仓本）建立和保持同步， （ii）建模-使用现代深度学习预测模型（如神经网络体系结构）来预测（或预测）音乐生成序列中的未来状态变化以及耦合振荡器序列中的先占过渡。希望这次讨论将为更好的“实时同步人机协作界面和交互”带来新的见识和研究。