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2022 | Buch

Music, Mathematics and Language

The New Horizon of Computational Musicology Opened by Information Science

verfasst von: Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka

Verlag: Springer Nature Singapore

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Über dieses Buch

This book presents a new approach to computational musicology in which music becomes a computational entity based on human cognition, allowing us to calculate music like numbers. Does music have semantics? Can the meaning of music be revealed using symbols and described using language? The authors seek to answer these questions in order to reveal the essence of music.

Chapter 1 addresses a very fundamental point, the meaning of music, while referring to semiotics, gestalt, Schenkerian analysis and cognitive reality. Chapter 2 considers why the 12-tone equal temperament came to be prevalent. This chapter serves as an introduction to the mathematical definition of harmony, which concerns the ratios of frequency in tonic waves. Chapter 3, “Music and Language,” explains the fundamentals of grammar theory and the compositionality principle, which states that the semantics of a sentence can be composed in parallel to its syntactic structure. In turn, Chapter 4 explains the most prevalent score notation – the Berklee method, which originated at the Berklee School of Music in Boston – from a different point of view, namely, symbolic computation based on music theory. Chapters 5 and 6 introduce readers to two important theories, the implication-realization model and generative theory of tonal music (GTTM), and explain the essence of these theories, also from a computational standpoint. The authors seek to reinterpret these theories, aiming at their formalization and implementation on a computer. Chapter 7 presents the outcomes of this attempt, describing the framework that the authors have developed, in which music is formalized and becomes computable. Chapters 8 and 9 are devoted to GTTM analyzers and the applications of GTTM. Lastly, Chapter 10 discusses the future of music in connection with computation and artificial intelligence.

This book is intended both for general readers who are interested in music, and scientists whose research focuses on music information processing. In order to make the content as accessible as possible, each chapter is self-contained.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Machine that Computes the Meaning of Music
Abstract
Until the medieval period, the composers of Western tonal music, when creating new pieces, first analyzed and understood the structures and styles of already existing pieces by themselves. Moving on to the eighteenth century, the public and critics appeared, and then the composers and the researchers of music theory were split.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 2. The Mathematics of Ebony and Ivory Keys
Abstract
‘Ebony and Ivory’ is a joint masterpiece of Paul McCartney and Stevie Wonder, representing black and white. ‘Is there any question?’ The lecturer asked the audience.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 3. Music as Formal Language
Abstract
What’s so special about music? First, all known cultures have had music—even those that have lacked a written language. It is as close to a universal human trait as you could hope for.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 4. Berklee Method
Abstract
The Berklee method originated as the music theory that was being taught at Berklee School of Music.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 5. Implication-Realization Model
Abstract
In this chapter, we will start by explaining about gestalt which appears in music, upon which the Implication-Realization model stands. Multiple basic patterns are defined in the Implication-Realization model, and at a glance these can seem to have been constructed ad hoc, but we shall demonstrate that actually they are constructed systematically.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 6. GTTM and TPS
Abstract
Schenkerian analysis claimed that there existed Ursatz (a fundamental structure) in each music piece, which was obtained by the process called reduction.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 7. Formalization of GTTM
Abstract
There are many types of information related to music, and there are various methods to represent such information, too. Among them, we assume the time-span tree of GTTM is promising for representing the listener’s cognition and the composer’s intention.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 8. Implementation of GTTM
Abstract
To get a music theory to operate on a computer, however, we must overcome some widely recognized fundamental difficulties. One is giving an ambiguous concept of a firm definition, and another is compensating for the lack of necessary concepts (externalization). For example, we may easily decide whether two melodies are similar to each other, but in general each of us likely makes a different decision, and it is difficult to fully explain why the melodies are similar.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Chapter 9. Application of GTTM
Abstract
We propose a new methodology of melodic morphing as a tenable and trustworthy form of music manipulation.
Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
Backmatter
Metadaten
Titel
Music, Mathematics and Language
verfasst von
Keiji Hirata
Satoshi Tojo
Masatoshi Hamanaka
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-19-5166-4
Print ISBN
978-981-19-5165-7
DOI
https://doi.org/10.1007/978-981-19-5166-4