Music in the Data

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A01=Christopher White
Author_Christopher White
Category=AVA
Category=AVLA
Chord Categories
Chord Vocabulary
computational musicology
Corpus Analysis
Cross Entropy
data analysis
digital humanities
empirical music research
Entity Sets
eq_art-fashion-photography
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_music
eq_nobargain
eq_non-fiction
Harmonic Function
harmonic syntax studies
Iv Chord
Key Profile
Major Triad
Metrical Accents
metrical structure analysis
Midi File
music analysis
music corpora
music datasets
music research
music theory
music traditions
Musical Corpus
Musical Surface
musicology
Note Attacks
Pitch Class Sets
Power Law Curve
Power Law Distribution
Quadruple Meter
quantitative analysis
quantitative approaches to tonal traditions
Salami Slice
Scale Degree
Seventh Chords
stylistic modeling
Surface Patterns
tonal analysis methods
Tonic Function
Tonic Triad
Western European Art Music

Product details

  • ISBN 9781032259222
  • Weight: 480g
  • Dimensions: 152 x 229mm
  • Publication Date: 28 Dec 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Putting forward an extensive new argument for a humanities-based approach to big-data analysis, The Music in the Data shows how large datasets of music, or music corpora, can be productively integrated with the qualitative questions at the heart of music research. The author argues that as well as providing objective evidence, music corpora can themselves be treated as texts to be subjectively read and creatively interpreted, allowing new levels of understanding and insight into music traditions.

Each chapter in this book asks how we define a core music-theory topic, such as style, harmony, meter, function, and musical key, and then approaches the topic through considering trends within large musical datasets, applying a combination of quantitative analysis and qualitative interpretation. Throughout, several basic techniques of data analysis are introduced and explained, with supporting materials available online. Connecting the empirical information from corpus analysis with theories of musical and textual meaning, and showing how each approach can enrich the other, this book provides a vital perspective for scholars and students in music theory, musicology, and all areas of music research.

Winner, Emerging Scholar Award (Book), Society for Music Theory, 2023

Christopher White is Assistant Professor of Music Theory at the University of Massachusetts Amherst.

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