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Towards Computational Analysis of Pansori Singing

Sangheon Park, Danbinaerin Han, Dasaem Jeong

TL;DR

Computational analysis of pansori based on both audio and corresponding transcription is introduced, how modern Music Information Retrieval tasks can be used in analyzing traditional music and how it revealed different audio characteristics of what pansori contains.

Abstract

Pansori is one of the most representative vocal genres of Korean traditional music, which has an elaborated vocal melody line with strong vibrato. Although the music is transmitted orally without any music notation, transcribing pansori music in Western staff notation has been introduced for several purposes, such as documentation of music, education, or research. In this paper, we introduce computational analysis of pansori based on both audio and corresponding transcription, how modern Music Information Retrieval tasks can be used in analyzing traditional music and how it revealed different audio characteristics of what pansori contains.

Towards Computational Analysis of Pansori Singing

TL;DR

Computational analysis of pansori based on both audio and corresponding transcription is introduced, how modern Music Information Retrieval tasks can be used in analyzing traditional music and how it revealed different audio characteristics of what pansori contains.

Abstract

Pansori is one of the most representative vocal genres of Korean traditional music, which has an elaborated vocal melody line with strong vibrato. Although the music is transmitted orally without any music notation, transcribing pansori music in Western staff notation has been introduced for several purposes, such as documentation of music, education, or research. In this paper, we introduce computational analysis of pansori based on both audio and corresponding transcription, how modern Music Information Retrieval tasks can be used in analyzing traditional music and how it revealed different audio characteristics of what pansori contains.

Paper Structure

This paper contains 10 sections, 4 figures.

Figures (4)

  • Figure 1: Overview of data visualization pipeline from the pansori singer to data.
  • Figure 2: Pitch histograms of two daemok, No. 3 and No. 10, which have different modes, Ujo and Gyemyeonjo. Histogram shows F0 contour counts, and note durations from MusicXML transcriptions.
  • Figure 3: F0 contours of selected patterns
  • Figure :