Abstract
This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology.
Highlights
This paper uses several techniques that permit to visualize and graph some aspects of a Classical composers’ similarity matrix computed by Georges (2017)
Spotify, Last.fm, YouTube and other music streaming platforms have algorithms proposing what an auditor may want to listen. These algorithms and their improvement are largely tributary to the field of music information retrieval (MIR), which develops innovative content, context- and user-based searching schemes, music recommendation systems, and novel interfaces to make the vast store of music available to all
Dendrograms for other periods (Medieval and Renaissance, and twentieth century music (Impressionism, atonal, dodecaphonic, neoClassical, neo-Romantic and ‘avant-garde’ composers)) and for all 500 composers are available from the authors upon request, to be studied from a deeper musicological angle
Summary
This paper uses several techniques that permit to visualize and graph some aspects of a Classical composers’ similarity matrix computed by Georges (2017). ‘visualizing’ or ‘translating’ the similarity matrix into clusters and mapping of composers is an important communication tool. Before embarking on this mapping project, it may be useful to provide some background on the motivation for building a composers’ similarity matrix. Spotify, Last.fm, YouTube and other music streaming platforms have algorithms proposing what an auditor may want to listen next. Spotify, Last.fm, YouTube and other music streaming platforms have algorithms proposing what an auditor may want to listen These algorithms and their improvement are largely tributary to the field of music information retrieval (MIR), which develops innovative content-, context- and user-based searching schemes, music recommendation systems, and novel interfaces to make the vast store of music available to all.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
