![]() Footnote 4 They use and compare the performance of a dozen similarity indices or measure of association commonly used in fields such as biogeography and biological systematics such as the first and second Kulczynski coeffecients ( 1927), the Jaccard coefficient ( 1901), the Dice coefficient ( 1945), the Simpson coefficient ( 1943), the Smith coefficient ( 1983), the binary distance coefficient (Sneath 1968), and the binomial index of dispersion \(\chi^\) statistic (Potthoff and Whittinghill 1966). On the other hand, if two composers have been influenced by very distinct sets of composers, then their music is likely to have little similarity. ![]() Footnote 3 In essence, they infer similarities among composers by assuming that if two composers share many of the same personal musical influences, their music will likely have some similarities. Data on personal musical influences are taken (and available) from ‘The Classical Music Navigator’ (Smith 2000 hereafter referred to as CMN) where each of the 500 composers of the database is associated with a list of composers who have had a documented positive influence on a subject composer. In this vein, Smith and Georges ( 2014) propose a statistical analysis that captures similarity across pairs of composers by mean of pairwise comparison of presence-absence of personal musical influences (e.g., other composers/influencers). Footnote 2 In this case, the unit of analysis may also be the ‘composer’ or the ‘artist/performer’, not just the ‘composition’. Context-based MIR is motivated by the fact that there are aspects not encoded in an audio signal or that cannot be extracted from it, but which are nevertheless important to human perception of music, for example, the cultural background of a composer. ( 2015) for popular music where they investigate the evolution of musical diversity and disparity and whether evolution has been gradual or punctuated. ( 2018) for classical music, and by Mauch et al. This particular angle has been used among others, by Foote ( 1999), Pampalk ( 2006), and more recently by Weiss ( 2017) and Weiss et al. The unit of study is typically the ‘composition’ and the analysis is to compare similarities/differences across audio signals from a series of compositions or from different segments of a same composition (self-similarity). Footnote 1Ĭontent-based MIR aims at uncovering from an audio signal meaningful music qualities (rhythm, timbre, melody, harmony, loudness, etc.) that can be used for music similarity and retrieval tasks. 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. Pandora, Spotify, Last.fm, YouTube and other music streaming platforms have algorithms proposing what an auditor may want to listen next. Yet, as mentioned by Smith ( 2000), most people explore new subjects by starting with the familiar, and in the case of music, this may mean hearing a composer that one likes and searching for more music of the same type. Many introductions to the classical music world are in the business of inculcation through lists of ‘mandatory’ composers and compositions to explore. However, the vast store of music makes the problem of what, exactly, to listen to, even more acute than during the pre-digital age. Through digital music files and streaming, classical music consumers have access to an enormous range of composers and styles of classical music. Before embarking on this mapping project, it may be useful to provide some background on the motivation for building a composers’ similarity matrix. Hence, ‘visualizing’ or ‘translating’ the similarity matrix into clusters and mapping of composers is an important communication tool. Sheer dimension prevents easy reporting of the results in a standard article. ![]() The number of composers analysed is 500 and the matrix is of dimension 500 × 500, leading to 250,000 pairwise (bilateral) composers’ similarity indices. This paper uses several techniques that permit to visualize and graph some aspects of a Classical composers’ similarity matrix computed by Georges ( 2017).
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