The Age of Big Data is here, and we in music studies find ourselves, as usual, both ahead of the curve and behind it. Like our cognate cousins in art history, film studies, and performance studies, we have reasonable excuse for lagging behind computational literati like Franco Moretti and Matthew Jockers, whose Stanford Literary Lab has been opening debates across the humanities1 and making headlines in the popular press. The excuse is technical: it is easy to transform literary texts into searchable data, and hard to do so for music. Insofar as the content of a novel is made out of words, it can be encoded as a long string of small numbers (representing letters and spaces) without losing any information. Even if we allow ourselves to believe that a musical score is an adequate representation of the content of a work, the encoding problem is vastly more complex....
Reviews: Hidden Structure: Music Analysis Using Computers and Music21: A Toolkit for Computer-Aided Musicology
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Ian Quinn; Reviews: Hidden Structure: Music Analysis Using Computers and Music21: A Toolkit for Computer-Aided Musicology. Journal of the American Musicological Society 1 April 2014; 67 (1): 295–307. doi: https://doi.org/10.1525/jams.2014.67.1.295
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