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Keeping Score: Computational Methods for the Analysis of Encoded ("Symbolic") Musical Scores

Authors: Gotham, Mark;

Keeping Score: Computational Methods for the Analysis of Encoded ("Symbolic") Musical Scores

Abstract

This book seeks to serve different users. It aims first and foremost to be an introductory textbook, usable by anyone, but particularly those with some experience with music theory or computation and a desire to learn more about the ‘other side’. As well as serving newcomers, learning for the first time, it also acts as a useful reference for the more experienced. No other resource of this kind exists to consolidate the field. Indeed, it was difficult simply to find all of the prior work cited here. Even I'm motivated to write it partly to create one ‘one stop shop’ for ease of reference. (Note: previously known as "Algorithms for Music Analysis and Data Science" AMADS)

Related Organizations
Keywords

Data Science, Algorithms, Music, Analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green