From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn’s The Lark

Jul 16, 2026

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Chamber music is treated as a precise multi-part interactive system driven by role assignment and dynamic interaction, offering a blueprint for human-computer collaborative composition. Focusing on the lack of role perception in existing deep music generation under polyphonic interaction, the study analyzes Haydn’s String Quartet in D Major, The Lark (Op. 64, No. 5). It proposes Classical Morphology Qualitative Analysis, Electroacoustic Quantitative Measurement, and Machine Representation Reconstruction, combining auditory counterpoint dissection with DAW-based objective physical parameters and introducing Event-based Timestamps and Role-Aware Encoding.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

Introduction

Research Background and the Necessity of an Interdisciplinary Perspective

Core Contributions of This Paper

Classical Perspective: Auditory Qualitative Analysis and Textural Separation

From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn’s The Lark Integrating Electroacoustic Analysis Yakun Liu, Zhiyu Jin, Hai Luan, Dong Liu, and Xiaonan Li arXiv :2607 .05902v 1 [ cs . SD] 7 Jul 2026 Abstract—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.” Index Terms—Role-Aware Encoding, Chamber Music Auditory Analysis, Electroacoustic Measurement, Machine Representation, Human-Computer Collaborative Aesthetics I. INTRODUCTION A. Research Background and the Necessity of an Interdisci plinary Perspective Chamber music, particularly the string quartet, functions asa conductor-less, highly precise multi-part interactive system that demands an exceptional degree of tacit ”listening” and”responding” among players [1] . This musical form, relying purely on communication across multiple roles, provides a Corresponding author: Xiaonan Li. Yakun Liu, Dong Liu, and Xiaonan Li are with the Department of Composition, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China. Zhiyu Jin is with the Department of Musicology, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China. Hai Luan is with the Education Information Center, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China. highly valuable closed research blueprint for exploring humancomputer collaborative composition paradigms. As a core genre established and brought to its zenith during the Classical period, its primary musical tension stems not merely from stacked harmonies, but from highly explicit and fluid role assignments and dynamic handovers. However, as artificial intelligence advances deeper into symbolic music generation [2], [3], the rapid surge of technicalism has masked fatal flaws in the underlying logic of these models. When handling multi-part music, mainstream deep learning models often reduce music to flat, non-interfering arrays of notes. Traditional algorithms heavily rely on a fixed grid for forced spatial quantization. This purely engineeringdriven approach brutally obliterates the micro-timing elasticity (rubato) crucial to chamber music [4], [5] and completely deprives the model of any role perception capability. To break this bottleneck in machine generation, an inter

FAQs

What problem does the paper address in deep music generation models?

It addresses the lack of role perception capability when models generate multi-part music under polyphonic interactions, where mainstream approaches flatten music into non-interfering note arrays and rely on fixed quantization grids.

What is the proposed research pathway in the study?

The pathway is “Classical Morphology Qualitative Analysis—Electroacoustic Quantitative Measurement—Machine Representation Reconstruction,” starting from auditory analysis of counterpoint texture and mapping it into objective measurable acoustic features.

How does the paper encode micro-timing and musical roles for machine representation?

It abandons the traditional mechanical quantization grid and introduces Event-based Timestamps to capture micro-timing duration, then converts acoustic features into an independent “Role-Aware Encoding” serving as an aesthetic heuristic mechanism.

Jul 16, 2026

Upload by Miles