发表机构
St. Petersburg State University; ITMO University; St. Petersburg State Institute of Culture（圣彼得堡国立大学; 圣彼得堡国立信息技术、机械与光学大学; 圣彼得堡文化学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究西方艺术音乐跨文化和声变化，提出从表示到动态的框架，通过一系列操作建模风格演变，应用于多国作品语料库，其轨迹和模式与音乐历史记载相符，为文化遗产研究提供定量层面。
AI 中文摘要
数字化音乐遗产收藏为研究风格传统如何随历史时间变化提供了新机会，但计算分析常将音乐作品简化为静态分类或相似度分数。本文提出一个从表示到动态的框架，用于研究西方艺术音乐中的跨文化和声变化。从符号和弦序列出发，得出上下文和弦嵌入，将作品级表示投影到共享和声空间，通过时间因果卡尔曼滤波重建国家层面的轨迹。用德格鲁特和弗里德金 - 约翰森动力学对这些轨迹建模，得到可解释的类似影响的网络和风格定位估计。将该框架应用于1875年至1940年来自俄罗斯、法国、德国、奥地利及一个异质“其他”组的480部注明日期作品的精选语料库。在1875 - 1925年拟合的模型通过对1930 - 1940年的递归预测进行评估，测试估计的依赖结构在潜在变化的历史和风格体系中是否仍具信息性。估计的轨迹和影响模式与已确立的晚期浪漫主义和早期现代主义交流的音乐历史记载大致一致。PCA方差加权估计在保留单个可解释影响网络的同时有适度改进。该框架为文化遗产研究提供了一个可重复的定量层面，补充了档案和音乐学解释。
英文摘要
Digitized musical heritage collections offer new opportunities to examine how stylistic traditions change over historical time, but computational analyses often reduce musical works to static classifications or similarity scores. This article proposes a representation-to-dynamics framework for studying cross-cultural harmonic change in Western art music. Starting from symbolic chord sequences, we derive contextual chord embeddings, project work-level representations into a shared harmonic space, and reconstruct country-level trajectories through temporally causal Kalman filtering. These trajectories are then modeled with DeGroot and Friedkin--Johnsen dynamics, yielding interpretable influence-like networks and estimates of stylistic anchoring. We apply the framework to a curated corpus of 480 dated works from 1875 to 1940 across Russia, France, Germany, Austria, and a heterogeneous ''Others'' group. Models fitted on 1875--1925 are evaluated through recursive forecasts for 1930--1940, testing whether the estimated dependency structure remains informative across a potentially changing historical and stylistic regime. The estimated trajectories and influence patterns are broadly consistent with established music-historical accounts of late Romantic and early modernist exchange, including the close relationship between German and Austrian traditions and historically plausible cross-currents between Russian and French traditions. PCA-variance-weighted estimation provides modest improvements while preserving a single interpretable influence network. Rather than treating the estimated matrices as direct causal evidence, the framework offers a reproducible quantitative layer for cultural heritage research, complementing archival and musicological interpretation.
