To ensure the quality and reusability of published research results, the TU Graz Repository has introduced a new curation workflow for research results.
Learn more about the workflow and features in the curation guide and try out the interactive demo.
For further questions and introductory training please contact the RDM Team at rdmteam[AT]tugraz.at!

Published in May 2025 | Version v1

Dissertation Open

SuziAI: digital encoding, optical music recognition, and stylistic generation for song dynasty chinese music in Jiang Kui's Baishidaoren Gequ

Repolusk, Tristan (aut)

95807.pdf

Files (11.6 MB)

Name	Size
95807.pdf md5:8bed83131dd44beebe7032bd2af32e8b	11.6 MB	Preview Download

Upload information

Created: August 6, 2025 | Modified: August 6, 2025

Name: Repolusk, Tristan

Title: SuziAI: digital encoding, optical music recognition, and stylistic generation for song dynasty chinese music in Jiang Kui's Baishidaoren Gequ

Resource Type: Dissertation

Published: May 2025

Language: eng