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The UbuWeb Electronic Music Corpus: An MIR investigation of a historical database

Published online by Cambridge University Press:  05 March 2015

Nick Collins
Nick Collins*
Affiliation:
Department of Music, Durham University, Palace Green, Durham, DH1 3RL
*
Email: nick.collins@durham.ac.uk
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Abstract

A corpus of historical electronic art music is available online from the UbuWeb art resource site. Though the corpus has some flaws in its historical and cultural coverage (not least of which is an over-abundance of male composers), it provides an interesting test ground for automated electronic music analysis, and one which is available to other researchers for reproducible work. We deploy open source tools for music information retrieval; the code from this project is made freely available under the GNU GPL 3 for others to explore. Key findings include the contrasting performance of single summary statistics for works versus time series models, visualisations of trends over chronological time in audio features, the difficulty of predicting which year a given piece is from, and further illumination of the possibilities and challenges of automated music analysis.

Type
Articles
Information
Organised Sound , Volume 20 , Issue 1: Special issue: Organised Sound Celebrates 20 Years , April 2015 , pp. 122 - 134
Copyright
© Cambridge University Press 2015 

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References

Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B. and Lamere, P. 2011. The Million Song Dataset. Proceedings of the 12th International Society for Music Information Retrieval Conference.Google Scholar
Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C. and Slaney, M. 2008. Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE 96(4): 668–696.CrossRefGoogle Scholar
Clarke, E. and Cook, N. (eds.) 2004. Empirical Musicology: Aims, Methods, Prospects. Oxford: Oxford University Press.CrossRefGoogle Scholar
Collins, N. 2011. SCMIR: A SuperCollider Music Information Retrieval Library. Proceedings of the International Computer Music Conference, 499–502.Google Scholar
Collins, N. 2013. Noise Music Information Retrieval. In A. Cassidy and A. Einbond (eds.) Noise In and As Music. Huddersfield: University of Huddersfield.Google Scholar
Griffiths, P. 1979. A Guide to Electronic Music. London: Thames and Hudson.Google Scholar
Harrison, J. and Wilson, S. (eds.) 2010. Sound <–> Space: New Approaches to Multichannel Music and Audio. Organised Sound 15(3): 183–184.CrossRefGoogle Scholar
Katz, B. 2007. Mastering Audio: The Art and the Science (2nd ed.). Oxford: Focal Press.Google Scholar
Klien, V., Grill, T. and Flexer, A. 2012. On Automated Annotation of Acousmatic Music. Journal of New Music Research 41(2): 153–173.Google Scholar
Landy, L. 2007. Understanding the Art of Sound Organisation. Cambridge, MA: MIT Press.Google Scholar
Manning, P. 2013. Electronic and Computer Music, 4th ed.New York: Oxford University Press.Google Scholar
Marsden, A. and Pople, A. (eds.) 1992. Computer Representations and Models in Music. London: Academic Press.Google Scholar
Park, T. H., Li, Z. and Wu, W. 2009. EASY Does It: The Electro-Acoustic Music Analysis Toolbox. Proceedings of the International Symposium on Music Information Retrieval, Kobe, Japan.Google Scholar
Pearce, M. and Wiggins, G. 2004. Improved Methods for Statistical Modelling of Monophonic Music. Journal of New Music Research 33(4): 367–385.Google Scholar
Pras, A., Zimmerman, R., Levitin, D. and Guastavino, C. 2009. Subjective Evaluation of MP3 Compression for Different Musical Genres. In Audio Engineering Society Convention 127, Audio Engineering Society.Google Scholar
Raffel, C. and Ellis, D. 2013. Reproducing Pitch Experiments in ‘Measuring the Evolution of Contemporary Western Popular Music’. Research report. Available from http://rrr.soundsoftware.ac.uk/reproducing-pitch-experiments-measuring-evolution-contemporary-western-popular-music.Google Scholar
Risset, J.-C. and Wessel, D. L. 1999. Exploration of Timbre by Analysis and Synthesis. In D. Deutsch (ed.) 1999. The Psychology of Music (2nd ed.). San Diego, CA: Academic Press.Google Scholar
Selfridge-Field, E. 1993. Music Analysis by Computer. In G. Haus (ed.) 1993. Music Processing. Oxford: Oxford University Press.Google Scholar
Serrà, J., Corral, Á., Boguñá, M., Haro, M. and Arcos, J. L. 2012. Measuring the Evolution of Contemporary Western Popular Music. Scientific Reports 2.Google Scholar
Sethares, W. A. 2005. Tuning Timbre Spectrum Scale (2nd ed.). Berlin: Springer Verlag.Google Scholar
Stowell, D. and Plumbley, M. D. 2007. Adaptive Whitening for Improved Real-Time Audio Onset Detection. In Proceedings of the International Computer Music Conference, Copenhagen, Denmark: 312–19.Google Scholar
Sturm, B. 2012. An Analysis of the GTZAN Music Genre Dataset. Proceedings of the Second International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies: 7–12.CrossRefGoogle Scholar
Virtanen, T. and Helén, M. 2007. Probabilistic Model Based Similarity Measures for Audio Query-By-Example. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New York: 82–5.Google Scholar
Wiggins, G., Miranda, E., Smaill, A. and Harris, M. 1993. A Framework for the Evaluation of Music Representation Systems. Computer Music Journal 17(3): 31–42.Google Scholar