Grammatical Structure and Grammatical Variations in Non-Metric Iranian Classical Music

2025-07-14

AI Summary

This study addresses the non-metric, rhythmically free melodic tradition of Iranian classical music—Dastgah Shour—which lacks standardized rhythmic frameworks, systematic structural analysis, and principled variant generation methods. We propose the first grammar-based framework for structural parsing and culturally grounded melody generation. Our approach constructs a symbolic repertoire dataset, designs a melody structure parsing algorithm, and introduces, for the first time in Eastern non-metric music, a grammar-driven mutation mechanism. Integrating MIDI-based symbolic modeling, structural parsing, and rule-based variation, the framework is validated through expert auditory evaluation and statistical analysis: generated variants conform to traditional grammatical constraints and achieve acceptable musical quality. Crucially, the framework demonstrates cross-cultural transferability, enabling adaptation to analogous classical systems—including Arabic Maqam and Turkish Makam traditions—where similar structural ambiguity and absence of metric scaffolding prevail.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation
• Machine Learning: Structured Learning

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this study we introduce a symbolic dataset composed of non-metric Iranian classical music, and algorithms for structural parsing of this music, and generation of variations. The corpus comprises MIDI files and data sheets of Dastgah Shour from Radif Mirza Abdollah, the foundational repertoire of Iranian classical music. Furthermore, we apply our previously-introduced algorithm for parsing melodic structure (Kanani et al., 2023b)to the dataset. Unlike much Western music, this type of non-metric music does not follow bar-centric organisation. The non-metric organisation can be captured well by our parsing algorithm. We parse each tune (Gusheh) into a grammar to identify motifs and phrases. These grammar representations can be useful for educational and ethnomusicological purposes. We also further develop a previously-introduced method of creating melodic variations (Kanani et al., 2023b). After parsing an existing tune to produce a grammar, by applying mutations to this grammar, we generate a new grammar. Expanding this new version yields a variation of the original tune. Variations are assessed by a domain-expert listener. Additionally, we conduct a statistical analysis of mutation with different representation setups for our parsing and generation algorithms. The overarching conclusion is that the system successfully produces acceptable variations post-mutation. While our case study focuses on Iranian classical music, the methodology can be adapted for Arabic or Turkish classical music.

Problem

• Analyzing non-metric Iranian classical music structure
• Developing algorithms for parsing and generating variations
• Adapting methodology for other classical music traditions

Innovation

• Symbolic dataset for non-metric Iranian music
• Grammar-based parsing for melodic structure
• Mutation-driven generation of melodic variations