INVITED SPEAKER

Prof. Takashi KUREMOTO, Nippon Institute of Technology, Japan

Takashi Kuremoto received the B.E. degree in System Engineering from the University of Shanghai for Science and Technology, China, in 1986, and the M.E. and Ph.D. degrees from Yamaguchi University, Japan, in 1996 and 2014, respectively. From 1986 to 1992, he worked as a system engineer at the Research Institute of Automatic Machine, Beijing. In 2008, he was an Academic Visitor at the School of Computer Science, The University of Manchester, U.K. He was affiliated with Yamaguchi University from 1993 to 2021, and since 2021 he has been a Professor at the Nippon Institue of Technology, Japan. His research interests include artificial neural networks, bioinformatics, machine learning, complex systems, time series forecasting, and swarm intelligence. He has authored more than 300 publications and is a member of IEICE, IEEE, IIF, and SICE.

Speech Title: Research on Restoring Guqin Music through Deep Learning

Abstract: Musical notation plays a central role in cultural transmission, yet many works remain unperformed due to the complexity of their notation systems. A notable example is the Jianzipu notation used for the Guqin, China’s oldest plucked string instrument, inscribed by UNESCO as Intangible Cultural Heritage in 2008. Jianzipu condenses finger movements and string assignments into single characters but omits rhythm and tempo, requiring performers to reconstruct the music through DaPu, a demanding interpretive process. Consequently, only about 100 of the 600 Guqin pieces composed over three millennia are actively performed today. To address this challenge, our study applies deep learning–based image recognition to automate DaPu and enhance cultural heritage preservation. Prior research has pursued two approaches: whole-character classification and component-level recognition. Building on these, we expand the dataset from 55 to 203 classes by including multiple pieces, notably Xian-Weng-Cao and Chun-Xiao-Yin. Using YOLOv11n, we extracted individual characters, then combined ResNet50 feature extraction with K-means clustering to classify 1,560 images efficiently. Data augmentation yielded a balanced dataset of 40,600 samples. Fine‑tuning ResNet50 achieved a recognition accuracy of 99.09%.