TY - GEN
T1 - Exploring the general melodic characteristics of XinTianYou folk songs
AU - Li, Juan
AU - Dong, Lu
AU - Ding, Jianhang
AU - Yang, Xinyu
N1 - Publisher Copyright:
© 2015 J. Li, L. Dong, J. Ding, X. Yang.
PY - 2015
Y1 - 2015
N2 - This paper aims to analyze one style of Chinese traditional folk song named Shaanxi XinTianYou. By analyzing the melody of this folksong genre, we make a clear, vivid, and thus easily approachable presentation of the cultural characteristics and significance of XinTianYou. Comparing to previous researches which mainly focus on mathematics and statistics, we further consider the musical continuity. Our insight is that, the combination of intervals reflects the characteristics of the music style. The significant pattern of the combinations can be used as representations of XinTianYou. We build a MIDI database, based on which the most representative combination of intervals are extracted. We propose to use N-Apriori algorithm which counts the frequent patterns of melody. Considering both the significance and similarity between music pieces, we provide a multi-layer melody perception clustering algorithm which uses both the melodic direction and the melodic value. The experiment results are analyzed based on both pattern mining techniques and music theories. For evaluation, we asked experts in this field to mark our results and proved that our results are consistent with the expert's intuition.
AB - This paper aims to analyze one style of Chinese traditional folk song named Shaanxi XinTianYou. By analyzing the melody of this folksong genre, we make a clear, vivid, and thus easily approachable presentation of the cultural characteristics and significance of XinTianYou. Comparing to previous researches which mainly focus on mathematics and statistics, we further consider the musical continuity. Our insight is that, the combination of intervals reflects the characteristics of the music style. The significant pattern of the combinations can be used as representations of XinTianYou. We build a MIDI database, based on which the most representative combination of intervals are extracted. We propose to use N-Apriori algorithm which counts the frequent patterns of melody. Considering both the significance and similarity between music pieces, we provide a multi-layer melody perception clustering algorithm which uses both the melodic direction and the melodic value. The experiment results are analyzed based on both pattern mining techniques and music theories. For evaluation, we asked experts in this field to mark our results and proved that our results are consistent with the expert's intuition.
UR - https://www.scopus.com/pages/publications/84988481802
M3 - 会议稿件
AN - SCOPUS:84988481802
T3 - Proceedings of the 12th International Conference in Sound and Music Computing, SMC 2015
SP - 393
EP - 399
BT - Proceedings of the 12th International Conference in Sound and Music Computing, SMC 2015
PB - Music Technology Research Group, Department of Computer Science, Maynooth University
T2 - 12th International Conference on Sound and Music Computing, SMC 2015
Y2 - 30 July 2015 through 1 August 2015
ER -