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This study analyzes Haydn's String Quartet in D Major, The Lark, to address the limitations of existing deep music generation models in recognizing roles during polyphonic interactions. By integrating auditory analysis with advanced electroacoustic measurement techniques, the authors propose a new method of "Role-Aware Encoding" that captures micro-timing and dynamic interactions in music. The key finding is that this interdisciplinary approach not only enhances the understanding of classical music structures but also lays the groundwork for more effective human-computer collaborative composition systems.
Role-Aware Encoding reveals how nuanced micro-timing in classical music can transform AI's approach to collaborative composition.
Chamber music, as a highly precise multi-part interactive system, contains a logic of"role assignment and dynamic interaction"that provides an extremely valuable blueprint for exploring human-computer collaborative composition paradigms. Addressing the lack of role perception capabilities in existing deep music generation models during polyphonic interactions, this paper conducts an interdisciplinary analysis of Haydn's String Quartet in D Major, The Lark (Op. 64, No. 5). We propose a novel research path:"Classical Morphology Qualitative Analysis-Electroacoustic Quantitative Measurement-Machine Representation Reconstruction."The study first utilizes auditory analysis to dissect the counterpoint morphology of the leading voice and the underlying groove in the first movement. Subsequently, it introduces spectrum and dynamic feature analysis tools from a Digital Audio Workstation (DAW) to translate subjective auditory perception into objective, measurable physical parameters. Building on this, the paper introduces a fundamentally new approach to low-level computer feature extraction: completely abandoning the traditional mechanical quantization grid, introducing Event-based Timestamps to record the duration of micro-timing, and transforming acoustic features into an independent"Role-Aware Encoding"as an aesthetic heuristic mechanism (a phenomenological anchor). This study not only completes the logical loop spanning classical analysis, electronic music mapping, and AI symbolic generation but also establishes a profound theoretical foundation-from the perspectives of interactive aesthetics and media philosophy-for constructing human-computer collaborative music systems imbued with"social attributes"and"otherness awareness."
