MPAG Begins Joint Research with KAIST's "MACLab"

Joint Research for Developing an AI-Based Sheet Music System
Tailored to Each Performer’s Skill Level

MPAG, a global digital sheet music trading platform operator, announced on May 16 that it has signed a joint research agreement with MACLab (Music and Audio Computing Lab) at KAIST to conduct research on an "AI-based sheet music system that determines performance difficulty."

Through this joint research, MPAG and KAIST's MACLab plan to leverage their respective expertise to develop an algorithm that predicts the difficulty level of piano performances and to focus on data analysis. They aim to build a system that adjusts the difficulty of sheet music according to the skill level of each performer.

MACLab is a music and audio computing research lab at KAIST's Graduate School of Culture Technology, led by Professor Nam Juhan. The lab has conducted various music AI research projects, including music information retrieval, audio signal processing, and music performance and generation.

MPAG and MACLab plan to utilize AI technology to automatically analyze the difficulty of sheet music, thereby improving accessibility to music education and providing tangible support for performers to enhance their skills. In particular, by employing technology that decomposes and digitizes sheet music at the note level, they aim to precisely reflect note arrangement, rhythmic complexity, and technical requirements according to the difficulty of the music, thereby implementing a highly sophisticated system.

Jung Inseo, CEO of MPAG, stated, "The research conducted with MACLab will open up new possibilities for technological convergence," and added, "Through this joint research, we plan to further strengthen our AI capabilities and deliver innovative value in the global digital sheet music industry."