GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling

2026-07-28

AI Summary

This work addresses the limitations of the original IDyOM model—namely its closed-source nature, opaque memory structure, and incompatibility with modern Python workflows—which hinder analysis and extension. We present the first graph-native reimplementation of IDyOM, explicitly modeling its long- and short-term predictive memory as graph objects. This approach faithfully preserves IDyOM’s variable-order, multi-view architecture while enabling information-theoretic computation, memory structure export, and local web-based interactivity. Our implementation not only replicates results from the original Lisp version across multiple configurations and outperforms recent reimplementations in performance but also facilitates memory network analysis and time-sensitive, interactive modeling of musical expectancy, offering both enhanced accessibility and extensibility.

Abstract

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.

Problem

• IDyOM
• musical expectation
• memory representation
• Python integration
• computational modeling

Innovation

• graph-native
• musical expectation
• memory representation
• variable-order modeling
• interactive analysis