Music Analysis by Computer: Ontology and Epistemology

This chapter examines questions of what is to be analysed in computational music analysis, what is to be produced, and how one can have confidence in the results. These are not new issues for music analysis, but their consequences are here considered explicitly from the perspective of computational analysis. Music analysis without computers is able to operate with multiple or even indistinct conceptions of the material to be analysed because it can use multiple references whose meanings shift from context to context. Computational analysis, by contrast, must operate with definite inputs and produce definite outputs. Computational analysts must therefore face the issues of error and approximation explicitly. While computational analysis must retain contact with music analysis as it is generally practised, I argue that the most promising approach for the development of computational analysis is not systems to mimic human analysis, but instead systems to answer specific music-analytical questions. The chapter concludes with several consequent recommendations for future directions in computational music analysis.

[1]  Geraint A. Wiggins,et al.  Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints in the Harmonization Problem , 2013 .

[2]  Michael Good,et al.  MusicXML for notation and analysis , 2001 .

[3]  Alan Marsden,et al.  Schenkerian Analysis by Computer: A Proof of Concept , 2010 .

[4]  Deborah Mawer Bridging the divide: embedding voice-leading analysis in string pedagogy and performance , 1999, British Journal of Music Education.

[5]  David Temperley,et al.  Introduction to the Special Issues on Corpus Methods , 2013 .

[6]  Bob L. Sturm The State of the Art Ten Years After a State of the Art: Future Research in Music Information Retrieval , 2013, ArXiv.

[7]  J. Stephen Downie,et al.  The music information retrieval evaluation exchange (2005-2007): A window into music information retrieval research , 2008 .

[8]  Geraint A. Wiggins,et al.  Auditory Expectation: The Information Dynamics of Music Perception and Cognition , 2012, Top. Cogn. Sci..

[9]  Anna M. Barry,et al.  Varese's 'Density 21.5': A Study in Semiological Analysis , 1982 .

[10]  David Meredith,et al.  Music analysis and Kolmogorov complexity , 2012 .

[11]  A. Pople,et al.  Theory, analysis, and meaning in music , 1996 .

[12]  Mervyn Cooke,et al.  Chamber Music , 1991 .

[13]  Rudolph Reti The Thematic Process in Music , 1978 .

[14]  David Meredith,et al.  Computing Pitch Names In Tonal Music: A Comparative Analysis of Pitch Spelling Algorithms , 2007 .

[15]  N. Goodman,et al.  Languages of art : an approach to a theory of symbols , 1979 .

[16]  Simon Dixon,et al.  Improving Music Genre Classification Using Automatically Induced Harmony Rules , 2010 .

[17]  R. Jackendoff,et al.  A Generative Theory of Tonal Music , 1985 .

[18]  J. Levinson,et al.  What a Musical Work Is , 1980 .

[19]  David Meredith Music Analysis and Point-Set Compression , 2014 .

[20]  Jean-Pierre Martens,et al.  Combining Musicological Knowledge About Chords and Keys in a Simultaneous Chord and Local Key Estimation System , 2014 .

[21]  Yizhao Ni,et al.  Using Online Chord Databases to Enhance Chord Recognition , 2011 .

[22]  Satoshi Tojo,et al.  Implementing “A Generative Theory of Tonal Music” , 2006 .

[23]  Bob L. Sturm The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use , 2013, ArXiv.