Research Applications

DiArMaqAr provides unprecedented tools for systematic investigation of maqāmic relationships while maintaining connection to conventional frameworks. This guide outlines key research applications and use cases.

Comparative Tuning System Analysis

The platform's integration of historical tuning systems enables comparative analysis of Arabic theoretical frameworks through consistent Persian-Arab-Ottoman note naming.

Research Questions

• How does the same maqām manifest across different temperaments?
• What are the consistencies and variations in theoretical approaches?
• How do historical tuning systems compare to modern frameworks?
• What are the practical implications of theoretical choices?

Methodology

1. Select a maqām (e.g., Maqām Rāst)
2. Compare across multiple tuning systems:
   • Al-Kindī (9th century ratios)
   • Al-Fārābī (10th century tunings)
   • Ibn Sīnā (11th century approaches)
   • Al-Ṣabbāgh (20th century comma-based system)
3. Analyze:
   • Pitch class values
   • Interval relationships
   • Available transpositions
   • Modulation possibilities

Example: Theoretical Evolution

Comparing Al-Fārābī's 10th-century ratios with Al-Ṣabbāgh's 20th-century comma-based system demonstrates:

• Theoretical evolution over time
• Maintenance of essential intervallic relationships
• Impact of measurement approaches on modal availability

Starting Note Convention Analysis

A crucial analytical capability lies in systematic comparison of tuning systems based on different starting note conventions. DiArMaqAr supports three starting note names: ʿushayrān, yegāh, and rāst.

Research Framework

Oud-Based Systems (ʿushayrān):

• Reflect the conventional tuning of the oud in perfect fourths (4/3)
• ʿushayrān marks the 1/1, corresponding to the lowest of the four strings tuned in perfect fourths — the two or more strings lower in pitch remain tuning-independent
• Examples: al-Kindī, al-Fārābī (oud conventions), al-Urmawī

Longnecked-Lute and Monochord/Sonometer Systems (yegāh/rāst):

• Derived from the fret divisions of longnecked lutes (Arabic tanbūr, Persian tar/sehtar, Turkish tanbur) or from monochord/sonometer measurements
• Follow a theoretical framework rather than a fixed instrument tuning
• Examples: al-Fārābī's Tanbūr al-Baghdādī and Tanbūr al-Khorasānī, through to the Tuning Committee of the 1932 Cairo Congress for Arabic Music

Al-Fārābī appears in both categories because he described multiple tuning systems derived from different instruments. When a tuning system begins from ʿushayrān versus yegāh (which are a 9/8 whole-tone apart), the resulting intervallic relationships, the availability of specific maqāmāt and ajnās, and the modulation pathways can vary substantially.

Research Applications

1. Availability Analysis:

   • Compare number of available maqāmāt
   • Compare number of available ajnās
   • Analyze transposition possibilities

2. Modulation Networks:

   • Compare modulation capabilities
   • Identify differences in pathway structures
   • Analyze theoretical accessibility

3. Historical Instrument Practice:

   • Understand how instrumental traditions affect theory
   • Reveal connections between practice and theory
   • Examine theoretical evolution

Quantitative Analysis

The platform's analytics capabilities provide quantitative insights into maqāmic relationships.

Dataset Generation

Export comprehensive datasets for:

• All maqāmāt in selected tuning systems
• All possible transpositions
• Complete modulation networks
• Ajnās compatibility matrices

Statistical Analysis

Correlation Studies:

• Relationship between tuning system characteristics and transposition possibilities
• Correlation between transposition availability and modulation networks
• Impact of starting note conventions on modal accessibility

Pattern Recognition:

• Systematic modulation structures
• Transposition frequency patterns
• Ajnās distribution analysis

Example Research

Analyse relationships between:

• Number of pitch classes and available maqāmāt
• Starting note convention and transposition possibilities
• Tuning system complexity and modulation network density

Concrete example (al-Shawwā modulation algorithm): For maqām bayyāt on its conventional tonic of dūgāh in al-Ṣabbāgh's 24-tone comma-based tuning system (1954), the al-Shawwā modulation algorithm identifies 41 valid modulation pathways, whereas al-Ṣabbāgh's book provides only 8 fixed modulation targets for the same maqām. See §6.3 of the accompanying article for the full analysis.

Musicological Research

The platform enables systematic analysis of the documented maqām tradition that would be extremely laborious through manual cross-referencing of dispersed, multilingual sources.

Repertoire Analysis (Forthcoming)

The platform's current scope is theoretical rather than practice-based. Integration with audio analysis for recorded performances is an active research direction, alongside the forthcoming downstream projects described in the accompanying article (§8):

• Arabic Maqām Identification (MIR): automatic maqām identification from audio recordings, using DiArMaqAr's JSON exports as verified theoretical reference data
• Arabic Maqām Networks: web-based visualisation and exploration of maqām construction and modulation networks across tuning systems
• Comparison between documented theory and performance practice, once audio-analysis capabilities are integrated

Maqām Naming and Classification

Theoretical Investigation:

• Analyse comma-based systems (e.g. al-Ṣabbāgh 1954)
• Understand the relationship between transposition and naming
• Investigate why certain maqām transpositions are given unique names even when sayr is not the differentiating factor

Arabic Music Pedagogy

Tuning and Intonation Analysis:

• Ground pedagogical discussions in historically documented Arabic tuning systems rather than 24-EDO by default
• Provide reference implementations that can support works which engage this subject critically (e.g. Farraj & Shumays 2019, chapter 11)
• Replace unreferenced assertions about Arabic intonation with source-backed pitch-class data
• Enable nuanced discussions beyond binary "quarter-tone vs. not quarter-tone" debates

Machine Learning and AI Applications

Training Data

• Ground truth labels: Validated, computationally accessible reference data
• Structured datasets: Ready for ML model training
• Provenance: Transparent source attribution
• Comprehensive coverage: Multiple tuning systems and historical frameworks

Model Development

• Maqām detection: Training data with verified labels
• Modulation prediction: Network data for sequence modeling
• Transposition analysis: Pattern recognition datasets
• Classification: Features based on theoretical structures

Dataset Construction

Address limitations in existing research:

• Documented ground truth methodology
• Multiple performer/reciter data
• Historical framework validation
• Culturally specific feature engineering

Computational Musicology

Systematic Analysis

• Network analysis: Modulation pathway structures
• Graph theory: Relationships between maqāmāt
• Pattern recognition: Recurring intervallic structures
• Comparative studies: Cross-cultural modal analysis

Infrastructure for Research

Address gaps identified by Gedik and Bozkurt (2009):

• Valid pitch-class definitions grounded in culturally specific frameworks
• Computationally accessible theory
• Verified reference data
• Transparent methodology

Instrument Design

Tuning System Analysis

• Mathematical precision: Exact pitch class values
• Historical frameworks: Authentic reference data
• Hardware integration: Scala export for synthesizers
• Software instruments: Accurate implementation of all intervals

Design Applications

• Digital instrument interfaces
• Software synthesizer design
• Hardware controller mapping
• Pedagogical instrument development

Pedagogical Research

Educational Applications

• Interactive learning: Real-time exploration of theory
• Comparative study: Multiple frameworks simultaneously
• Visual-auditory integration: See and hear relationships
• Systematic exploration: Comprehensive coverage

Learning Outcomes

• Understand theoretical evolution
• Compare historical approaches
• Hear mathematical relationships
• Explore beyond simplified models

Academic Citation

All data exports include:

• Complete bibliographic references: Ready for academic citation
• Source and page numbers: Precise attribution
• Historical context: Temporal framework
• Scholarly verification: Enables replication

Next Steps

• Explore Bibliographic Sources for citations
• Learn about Data Export capabilities
• Understand Cultural Framework methodology