Statistical Analysis of the features of diatonic music using jMusic software
Brown, Andrew R., Towsey, Michael W., Wright, Susan K., & Deiderich, Joachim (2001) Statistical Analysis of the features of diatonic music using jMusic software. In Computing ARTS 2001 – Digital resources for research in the Humanities, 26-28th September, 2001, Sydney, Australia: The University of Sydney..
The use of computers for analysis of diatonic melodies can be useful in the identification of interesting features often unobservable with manual analysis and provides a vehicle for the comparative analysis of individual melodies or classes of melodies. This paper presents our work in melodic feature analysis based on simple rules of diatonic melody writing. Through the testing of these features against a data set of melodies from Western music history we show which features are closely or loosely adhered to by composers in practice. We also show how individual melodies can be compared against the norms to highlight interesting characteristics for further manual analysis. Our music analysis software described in this paper makes the task of feature analysis relatively effortless, and its graphical presentation of results enables efficient and multi-modal communication of the data. We outline the basic operation of this software and provide details enabling others to access and perhaps modify the software for their needs. For example, one area of extension would be the provision of correlation between features. The computer has proved to be useful tool in focussing our thinking about diatonic music (in particular melodic construction), assisting with the analysis of large data sets, and in clarifying heuristics for algorithmic computational music creation.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Additional Information:||The contents of this conference can be freely accessed online via the conference's web page (see hypertext link).|
|Keywords:||statistical analysis, automation, music|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)|
Australian and New Zealand Standard Research Classification > STUDIES IN CREATIVE ARTS AND WRITING (190000) > PERFORMING ARTS AND CREATIVE WRITING (190400) > Musicology and Ethnomusicology (190409)
|Divisions:||Current > QUT Faculties and Divisions > Creative Industries Faculty|
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2001 (please consult author)|
|Deposited On:||17 Jul 2008|
|Last Modified:||09 Jun 2010 22:40|
Repository Staff Only: item control page