
doi: 10.1145/2583114
This article introduces the study contents and some research findings regarding digital preservation methods for Chinese Kunqu opera libretto historical literature, including historical literature electronic libretto transformation, libretto musical score image segmentation, musical information recognition, musical score information representation, musical score information storage, and libretto reconstruction on the Web. It proposes a novel editable text method to represent the multidimensional tree-like information structure of the Kunqu libretto literature and a musical semantic annotation method based on numbered musical notation to accommodate the musical features of Kunqu librettos. To maintain the characteristics of the original Kunqu musical notation, it proposes a method to reconstruct Kunqu libretto on the Web based on scalable vector graphics. Some Kunqu librettos were randomly selected for experiments, and the results demonstrated that the editable text method and the musical semantic annotation method were able to fully represent the effective information of the Kunqu libretto literature and that the method to reconstruct librettos on the Web was able to reflect the writing characteristics of the musical notation in the original librettos. Finally, it discusses the primary future research directions related to digital Kunqu , including Kunqu libretto metadata research, corpus construction for the librettos and Qupai (the unique ancient Chinese tune mode), libretto music information disambiguation research, libretto image segmentation and pattern recognition, digital Kunqu roles, digital Kunqu stages, digital Kunqu costume suitcases, virtual Kunqu , digitization and restoration of Kunqu cultural relics, and Kunqu 's application prospects in conventional media such as animation, anime, and movies.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
