ScoreSync AI

ScoreSync is a research-driven artificial intelligence platform dedicated to the study, analysis, and creative generation of Thai music using advanced Deep Learning techniques. The project is designed to bridge traditional musical knowledge with modern computational intelligence, enabling AI systems to understand musical structure, rhythm, melody, and cultural context at a deeper level.

ScoreSync serves as both a research framework and a creative system, supporting academic exploration while opening new possibilities for digital preservation and contemporary interpretation of Thai music.

Vision

The vision of ScoreSync is to become a reliable, extensible, and academically grounded AI system for Thai music research. The project emphasizes musical accuracy, theoretical integrity, and technical scalability, ensuring that the system can support long-term research, education, and creative applications.

Rather than replacing human creativity, ScoreSync is developed as an intelligent assistant—a tool that empowers musicians, researchers, and students to explore, analyze, and create music with deeper insight.

Model Architecture and Development

ScoreSync is currently implemented as a Deep Learning–based AI model, specifically designed for musical data. The model focuses on learning temporal sequences, melodic structures, rhythmic patterns, and symbolic representations of music.

Core and Additional Capabilities

Music Generation

Produces original musical compositions based on learned stylistic and structural patterns.

Music to Note

Converts audio-based musical input into symbolic note representations for analysis and study.

Note to Music

Synthesizes musical audio from structured note or score-based inputs.

Change Musical Instrument

Transforms musical output by simulating different instrumental timbres while preserving melodic identity.

Additional Functions

Modular extensions designed to support future research directions and experimental applications.

Role of ScoreSync in Thai Music Research

ScoreSync is not merely a technical experiment, but a research-oriented AI system intended to support:

• In-depth structural analysis of Thai music
• Educational tools for music learning and composition
• Digital preservation of traditional musical knowledge
• Creative exploration of new musical forms grounded in traditional foundations

Through computational modeling, ScoreSync contributes to the evolving dialogue between cultural heritage and artificial intelligence.

The Development of ScoreSync

By James Louis Gordovez

ScoreSync was developed as part of an academic research initiative aimed at integrating musical artistry with computational intelligence. The project reflects a multidisciplinary approach combining music theory, computer science, and artificial intelligence.

Acknowledgements

I would like to express my deepest gratitude to Associate Professor Dr. Pornprapit Phoasavadi, whose expertise, understanding, and patience greatly enriched my graduate experience. Her extensive knowledge and guidance were invaluable throughout this challenging research process.

I am also profoundly grateful to Associate Professor Nagul Cooharojananone for his invaluable advice and insights in the field of programming. His expertise played a critical role in strengthening my technical foundation and significantly contributed to the success of this research.

I sincerely thank the faculty and staff of the Faculty of Fine and Applied Arts, Chulalongkorn University, for their insightful feedback, encouragement, and continuous academic support.

Special thanks are extended to my colleagues Kanyagorn Ninyakanon, Pawichaya Aphiwatchaiyasiri, and Chalita Thueaksriwichai, whose collaboration, support, and exchange of ideas were immensely beneficial throughout the research journey.

I am deeply grateful to my family and friends—especially Arthur Neri Gordovez, Tina Ordanel Gordovez, John Lloyd Gordovez, and Art Cristian Gordovez—for their unwavering support and constant encouragement throughout my years of study.

Finally, I would like to thank the communities and individuals who participated in this study. Their contributions and insights were essential to the depth and direction of this research.