Music Data Mining

2011 ◽  
pp. 3-42 ◽  
Author(s):  
Tao Li ◽  
Lei Li
Keyword(s):  
Data Mining ◽  
Music Data Mining

Big Data Visualisation and Visual Analytics for Music Data Mining

2018 ◽  
Author(s):  
Katrina E. Barkwell ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Ashley A. Ocran ◽  
Jennifer M. Sanderson ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Visual Analytics ◽  
Data Visualisation ◽  
Music Data Mining

Mining in Music Databases

2011 ◽  
pp. 340-374
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

scholarly journals Ledhug: Syncretic Music Culture in Magetan

2020 ◽  
Vol 14 (2) ◽  
pp. 1-12
Author(s):  
Dea Lunny Primamona ◽  
Dolly Nofer
Keyword(s):  
Data Mining ◽  
Music Performance ◽  
Agricultural Products ◽  
Music Culture ◽  
Music Festival ◽  
Data Presentation ◽  
Mining Methods ◽  
Presentation Methods ◽  
Music Data Mining ◽  
Musical Elements

In Magetan, East Java, Indonesia, the turn of the Javanese and Islamic years is marked by the celebration of Ledhug Sura. The celebration consisted of various events such as the Ledhug music festival, the people's market, the carnival, the night of repentance and the climax at the Andum Berkah Bolu Rahayu procession. This is a carnival for cakes and agricultural products which are considered to bring blessings to the community. This research focuses on the ledhug (lesung and bedhug, which means a combination of mortar and drum) music festival. Mortar and bedhug are the main focus of this article. The purpose of this study is to (1) reveal the form of the conception of ledhug music and (2) recognize the concepts contained in the ledhug music. Data mining methods include fieldwork using observation and interview techniques; literature review; and data presentation methods using descriptive-interpretative techniques. Based on the results of fieldwork and reading or interpretation of the ledhug music culture, it was concluded that the ledhug music performance was the result of a process of cultural syncretism. Ledhug music culture exists as a blend of musical elements from Javanese and Islamic music culture. Cultural syncretism in the context of ledhug is a deliberately produced discourse building, namely as a form of resolution of the reality of cultural contestation between the two ideologies that existed in Magetan.

scholarly journals Trajectory of Fifths in Music Data Mining

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dariusz Kania ◽  
Paulina Kania ◽  
Tomasz Lukaszewicz
Keyword(s):  
Data Mining ◽  
Music Data Mining

Mining in Music Databases

2009 ◽  
pp. 35-59
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

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