This project based at Durham University was funded as a 10-month mini-project within the large-scale research project Transforming Musicology, funded under the AHRC’s Digital Transformations scheme, seeking to explore the transformation of musicology through the application of software tools such as those emerging from research in Music Information Retrieval.
Electronic music has a rich history over many decades, most intensively since the Second World War, with manifestations in art music and popular music spheres, and much experimental work in-between. The strong heritage of electronic music has been an increasing target of analysts.
MIR tools offer the possibility to expand this endeavour to a larger database of historical recorded works, tracking audible key trends in compositional and technological endeavour, with an empirical methodology. Ironically, despite the machine-mediated creation of electronic music, automated analysis techniques have not previously been employed to any great degree. Admittedly, although inexhaustible and objective, machine audio analysis has certain limitations compared to the golden standard of human listening.
The project team compiled a larger corpus of historical works, aiming for clear coverage of important works in electronic music history, with a balanced approach to experimental art music, and popular music works. The dual aims were for deeper musicological insight and theory construction on the one hand, but also to provide a core resource for future electronic music scholarship; the set of audio feature data has been released with an accompanying article in the new Transactions of ISMIR journal.
As part of the project, we’ve given a number of presentations, in Oxford to the enclosing transforming musicology project, in Leicester and Durham for research seminars, and in Huddersfield to a complementary project called TaCEM (www.hud.ac.uk/research/researchcentres/tacem/). During talks with researchers on that project, we discovered through machine analysis a tripartite structure in Trevor Wishart’s Imago (2002) that leapt out of a similarity matrix plot, illustrating that the machine analysis route can provide results of interest to human analysts deeply immersed in specific works.
Collins, N., Manning, P. & Tarsitani, S., (2018). A New Curated Corpus of Historical Electronic Music: Collation, Data and Research Findings. Transactions of the International Society for Music Information Retrieval. 1(1), pp.34–43.
Accompanying data: http://composerprogrammer.com/emcorpus.html