The Music in the Data: Corpus Analysis, Music Analysis, and Tonal Traditions

326 Pages·2022·11.854 MB·English

by  Christopher White

THE MUSIC IN THE DATA
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.
Christopher White is Assistant Professor of Music Theory at the University of
Massachusetts Amherst.

THE MUSIC IN THE DATA
Corpus Analysis, Music Analysis,
and Tonal Traditions
Christopher White
Designed cover image: amiak/Shutterstock
First published 2023
by Routledge
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and by Routledge
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Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 Christopher White
The right of Christopher White to be identified as author of this work
has been asserted in accordance with sections 77 and 78 of the Copyright,
Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other means,
now known or hereafter invented, including photocopying and recording,
or in any information storage or retrieval system, without permission in
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Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
Library of Congress Cataloging-in-Publication Data
Names: White, Christopher (Christopher Wm.) author.
Title: The music in the data : corpus analysis, music analysis, and
tonal traditions / Christopher White.
Description: [1.] | New York : Routledge, 2022. | Includes bibliographical
references and index.
Identifiers: LCCN 2022030364 (print) | LCCN 2022030365 (ebook) |
ISBN 9781032259222 (paperback) | ISBN 9781032259239 (hardback) |
ISBN 9781003285663 (ebook)
Subjects: LCSH: Musical analysis. | Music—Mathematics. | Musicology—
Data processing.
Classification: LCC MT90 .W52 2022 (print) | LCC MT90 (ebook) |
DDC 781.1—dc23/eng/20220808
LC record available at https://lccn.loc.gov/2022030364
LC ebook record available at https://lccn.loc.gov/2022030365
ISBN: 978-1-032-25923-9 (hbk)
ISBN: 978-1-032-25922-2 (pbk)
ISBN: 978-1-003-28566-3 (ebk)
DOI: 10.4324/9781003285663
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Access the Support Material: www.routledge.com/9781032259222
To Rob.
I wouldn’t want to be doing this with anyone but you.
I love you, and I’m so proud of you.

CONTENTS
Acknowledgments viii
1 Introduction and Methodology: Corpus Analyses and
Music Theory 1
2 What Is Style? An Essay 54
3 What Is Harmony? A Narrative 96
4 What Is Function? An Epistolary 137
5 What Is Meter? A Dialogue 189
6 What Is Key? A Diatribe 245
Index 310
ACKNOWLEDGMENTS
Many thanks to the music-theory graduate students of Yale University and
Florida State University, Ian Quinn, Nick Shea, Megan O’Harra, Kathleen
Coker, Samantha Franciosa, Iliana Fuentes Ordaz, Jess Racco, Eric Elder,
Tanushree Agrawal, David Temperley, Jason Yust, and the Lab Group (you know
who you are!) for feedback and support on this project.
1
INTRODUCTION AND
METHODOLOGY
Corpus Analyses and Music Theory
Let’s begin with a hypothetical tale of potential plagiarism. The story features
an underground electronic dance music (EDM) producer who specializes in
composing original tracks for house parties, small festivals, clubs, and the like.
One day, the Producer listens to a new song that a famous and established
Emcee just dropped. With surprise, they recognize one of the melodies. After
listening to the track a few times, their suspicions are confirmed: the melody
is identical to one the Producer had written several years ago. Furious with this
apparent and blatant musical theft, the Producer contacts an intellectual property
lawyer, who in turn files a plagiarism lawsuit against the Emcee. After assembling
their own legal team, the Emcee retorts that the supposedly stolen melody is
too simple to be covered under copyright – the melody is so generic that anyone
writing in the EDM style could stumble upon this sequence of pitches. Of
course, the Producer vehemently disagrees. Both teams then attempt to quantify
their claims in order to prove their arguments. Each seeks to show how likely
the melody was to spontaneously arise in the EDM repertoire – was it likely
or unlikely that both musicians would have independently produced the same
melody?
To answer this question, the litigators might use a corpus – a large dataset – of
EDM melodies. As our story stipulates that the Emcee’s track was released several
years after the producer wrote their melody, a relevant corpus might include
EDM hits that span those years. Such a dataset would quantify some baseline
norms within that style: melodic characteristics common to this dataset could
be imagined as prevalent in mainstream EDM music in that time period.
We can play out this hypothetical courtroom drama using a corpus of EDM
instrumental melodies I compiled along with one of my graduate students,
Poe Allphin. The dataset – what we call the Top-EDM corpus – consists
DOI: 10.4324/9781003285663-1