GraphIDyOM：用于音乐期望建模的IDyOM的原生Python重新实现
GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling
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中文总结 AI 辅助
研究针对IDyOM难以与Python集成及内存结构不易处理的问题，提出GraphIDyOM进行重新实现，保留其架构，验证了实现效果并与其他实现做了对比，展示了新实现支持多种分析及应用，为研究音乐期望提供平台。
中文摘要 AI 辅助
音乐信息动力学模型（IDyOM）在音乐期望的计算研究中发挥了核心作用，能逐事件估计符号音乐序列的不确定性和惊喜度。但其参考实现难以与当代Python工作流程集成，内部内存结构不易检查或修改。我们引入GraphIDyOM，它是IDyOM的原生Python重新实现，将长期和短期预测记忆表示为显式图形对象，同时保留模型的可变顺序、多视角架构。GraphIDyOM返回逐事件信息内容和熵，公开内部内存结构以供分析和导出，并支持通过本地服务器访问。我们针对原始Lisp IDyOM在单视角、投影和多视角配置下验证了该实现，并与最近的重新实现进行了覆盖范围和计算性能的基准测试。然后展示了显式内存表示如何支持对学习记忆的网络分析、将期望值投影到音乐网络上、近因敏感的内存检索和交互式应用。因此，GraphIDyOM既提供了一个广泛使用的模型的忠实且可访问的重新实现，又提供了一个通过记忆、拓扑和交互来研究音乐期望的平台。
英文摘要
The Information Dynamics of Music model (IDyOM) has played a central role in computational accounts of musical expectation by providing event-by-event estimates of uncertainty and surprise from symbolic musical sequences. However, its reference implementation is difficult to integrate with contemporary Python workflows, and its internal memory structures are not easily accessible for inspection or modification. We introduce GraphIDyOM, a graph-native Python reimplementation of IDyOM that represents long-term and short-term predictive memories as explicit graph objects while preserving the model's variable-order, multiple-viewpoint architecture. GraphIDyOM returns event-wise information content and entropy, exposes internal memory structures for analysis and export, and supports access through a local server. We validate the implementation against the original Lisp IDyOM across single, projected, and multiple-viewpoint configurations, and benchmark its coverage and computational performance against a recent reimplementation. We then demonstrate how the explicit memory representation supports network analysis of learned memories, projection of expectation values onto musical networks, recency-sensitive memory retrieval, and interactive applications. GraphIDyOM therefore provides both a faithful and accessible reimplementation of a widely used model and a platform for studying musical expectation through memory, topology, and interaction.
发表机构
- Institute for Interdisciplinary Studies on Artificial Intelligence (IRIDIA), Université Libre de Bruxelles（布鲁塞尔自由大学跨学科人工智能研究所（IRIDIA））
机构由 AI 辅助整理，请以论文原文为准。
