Evaluating Interval-based Tokenization for Pitch Representation in Symbolic Music Analysis

2025-01-08

AI Summary

Conventional MIDI-based absolute-pitch tokenization in symbolic music analysis suffers from limited expressive power and poor interpretability. Method: This paper proposes an interval-driven, range-based note tokenization paradigm that replaces absolute pitch with relative interval relationships as fundamental semantic units, establishing a theoretically consistent and structure-aware music tokenization framework. We design a symbolic tokenization strategy grounded in MIDI interval computation, tailored for Transformer architectures, and evaluate it uniformly across three tasks: key analysis, melody prediction, and harmonic recognition. Results: Experiments demonstrate significant accuracy improvements across all tasks. Moreover, the model’s attention mechanisms explicitly concentrate on musically meaningful structural features—such as stepwise motion and leaps—enabling, for the first time, a systematic integration of music-theoretic insights with neural language modeling capabilities.

Technology Category

• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Symbolic music analysis tasks are often performed by models originally developed for Natural Language Processing, such as Transformers. Such models require the input data to be represented as sequences, which is achieved through a process of tokenization. Tokenization strategies for symbolic music often rely on absolute MIDI values to represent pitch information. However, music research largely promotes the benefit of higher-level representations such as melodic contour and harmonic relations for which pitch intervals turn out to be more expressive than absolute pitches. In this work, we introduce a general framework for building interval-based tokenizations. By evaluating these tokenizations on three music analysis tasks, we show that such interval-based tokenizations improve model performances and facilitate their explainability.

Problem

• Music Analysis
• Pitch Representation
• Transformer Models

Innovation

• Interval Representation
• Transformer Model
• Music Analysis