03287nas a2200385   4500000000100000008004100001260005900042653003600101653002600137653001300163653003000176653003600206653002400242653001900266653001800285653002000303653001800323653002300341653002200364653003400386653003000420653001900450653002500469653001400494653002800508653003100536653001800567653003000585100001500615245008600630856015400716300001200870520199400882020002502876                                       d  bInstitute of Electrical and Electronics Engineers Inc.10aArtificial intelligence methods10aBayes decision theory10aBig data10aChinese traditional music10aClassification (of information)10aClustering analysis10aComputer music10aData handling10aDecision theory10aDeep learning10aGenetic algorithms10aGong-che notation10aIntangible cultural heritages10aIntegrated classification10aNetwork layers10aSemantic information10aSemantics10aSimilarity measurements10aSpatiotemporal information10aDeep learning10aoptical music recognition1 aG.-F. Chen00aMusic sheet score recognition of Chinese Gong-che notation based on Deep Learning  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115122000&doi=10.1109%2fBDACS53596.2021.00048&partnerID=40&md5=e1e08dc9f8ba0eff799695f312812028  a183-1903 aGong-che notation (GCN) is a major recording method for traditional Chinese music with a long history, first appearing in the late Tang dynasty and the period of the Five Dynasties in China. After an extensive developmental period, it matured during the Ming and Qing dynasties in China and became widely used in traditional Chinese music such as Chinese Kunqu opera, which has a rich heritage. In 2001, it was the first to be declared as a  Masterpiece of the Oral and Intangible Cultural Heritage of Humanity  by UNESCO. A GCN musical score contains meaningful semantic information, including Puzi, lyrics, tune, name, author name, tonality, and notes that are organized in many different styles depending on the particular composition. Collections of the score also contain artifact noise, such as stains and borders. This paper presents a multi-layer integrated classification network to extract semantic information from GCN musical scores by following traditional Chinese music theory combined with an analysis of spatiotemporal information and GCN criterion. The network consists of a multi-layer classifier, which identifies different classification objects by applying various different artificial intelligence methods at each level via clustering analysis, Bayes decision theory, genetic algorithm, deep learning, etc. According to the semantic assessment of the music information of the GCN musical score, this project proposed a similarity measurement method by comparing the manual annotation results and then optimizing the multi-layer integrated classification network. This paper studies the information mining of Chinese traditional musical notation using an interdisciplinary approach and presents a series of methods and schemes to develop and transmit Chinese traditional music in the digital age. It can help protect the proclaimed masterpieces of the oral and intangible heritage of humanity and promote the development of Chinese traditional art and arts technology.  a9781665425612 (ISBN)