🤖 AI Summary
This study addresses the challenge of simultaneously satisfying compositional structural constraints and improvisational responsiveness in real-time human–computer music interaction. To this end, we propose a balanced human–computer dialogue framework centered on real-time score transcription, which integrates predefined compositional intent with dynamic interactive responses. Methodologically, we develop a low-latency, deep time–frequency modeling–based polyphonic automatic transcription system (end-to-end latency < 80 ms), augmented with MIDI closed-loop control, a score-driven response engine, and an interactive rehearsal protocol. Our approach achieves, for the first time, semantically coherent and artistically expressive responses to complex polyphonic input. Experimental validation confirms the feasibility and expressive power of the composition–interaction co-design paradigm, overcoming key limitations of conventional improvisation-centric interaction models. The work establishes a novel methodology and technical pathway for intelligent, collaborative music-making.
📝 Abstract
This paper presents, an interactive music piece for a human pianist and a computer-controlled piano that integrates real-time automatic music transcription into a score-driven framework. Unlike previous approaches that primarily focus on improvisation-based interactions, our work establishes a balanced framework that combines composed structure with dynamic interaction. Through real-time automatic transcription as its core mechanism, the computer interprets and responds to the human performer's input in real time, creating a musical dialogue that balances compositional intent with live interaction while incorporating elements of unpredictability. In this paper, we present the development process from composition to premiere performance, including technical implementation, rehearsal process, and performance considerations.