Music Data Analysis
eBook - ePub

Music Data Analysis

Foundations and Applications

Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph, Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph

  1. 676 páginas
  2. English
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eBook - ePub

Music Data Analysis

Foundations and Applications

Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph, Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph

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Información del libro

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.

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Información

Año
2016
ISBN
9781315353838
Chapter 1
Introduction
WEIHS CLAUS, DIETMAR JANNACH, IGOR VATOLKIN, GüNTER RUDOLPH
TU Dortmund, Germany
1.1 Background and Motivation
Whenever we listen to a piece of pre-recorded music today, it is, almost with certainty, a playback of a digital recording. This is not surprising, since music has been distributed in digital form since the 1980s on compact discs. Since then, we have observed major disruptions in the music sector. Today, with the advances in the context of media encoding formats, higher processing power even on small devices, and high-bandwidth Internet connectivity, many of us no longer have physical music collections anymore but carry our virtual collections with us on our smartphones.
But this digitization has not only changed the way music is distributed and how we consume it, many other applications became possible since music can be easily digitally processed by computers. Today, various online music platforms automatically generate personalized radio stations based on your favorite tracks or recommend new music that sounds similar to your favorites. Other online services help to identify a certain piece of music based on the hummed melody. Your mobile music player, finally, probably tries to automatically organize your music collection based on the musical similarity of the tracks.
Many of these applications are based on the results of a music analysis process. The goal of these analysis processes typically is to automatically extract characteristic features of the musical pieces. These features can, e.g., be used to find similar tracks since they include characteristics of tempo and key, the instruments that are played, or even the mood that is conveyed by a track.
This book introduces the reader to the foundations of such music analysis processes and sketches the most prominent types of applications that can be built on these analyses. Furthermore, it provides the reader with the background knowledge required throughout the paper, e.g., in terms of acoustics, music theory, signal processing, statistics, and machine learning.
1.2 Content, Target Audience, Prerequisites, Exercises, and Complementary Material
Content and Target Audience This book is a university-level textbook and provides self-contained and interdisciplinary material for different target audiences. The primary audiences are university classes with a topic related to music data analysis, e.g., in the fields of computer science and statistics, but also in musicology and engineering.
The main features of this first comprehensive and self-contained book on music data analysis can be summarized as follows.
• The book covers both the foundations of music – including acoustics, physics and the human perception – as well as the basics of modern data analysis and machine learning techniques and the corresponding evaluation methodology.
• Based on these foundations, the book discusses various applications of music data analysis in depth including music recommendation, transcription and segmentation as well as instrument, chord, and tempo recognition.
• Finally, the book also covers implementation aspects of music data analysis systems including their architecture, user interface, as well as hardware-related issues.
Prerequisites, How to Read the Book, Exercises, and the Supporting Web Page Basic mathematics and, for the exercises, basic programming skills – preferably in R or MATLAB – are the only recommended prerequisites. Obviously, being able to read a musical score is fundamental. Throughout the book, we will provide additional pointers to further literature.
The book is designed for readers with heterogeneous backgrounds. When you prefer to approach the field from the application perspective, you might probably start reading one of the corresponding chapters in the third part of the book. Pointers to the underlying terminology, methodology, and algorithmic approaches, which are described in the first two parts of the book, will be given within these application chapters. Some chapters furthermore include short technical “interludes” for the advanced reader. You might skip these details in case you are rather interested in a general understanding of the subject.
If you want to test your understanding of material presented in the book, you may want to try some exercises. The book itself does not include exercises. However, theoretical as well as practical exercises based on R and MATLAB will be provided at the book’s web site http://sig-ma.de/music-data-analysis-book, which also includes example data sets partly needed for the exercises and errata.
Relation to Other Books A number of books focusing on Music Data Analysis appeared in the last ten years, among them [1], [2], [5], [3], [4], and [6].
Almost all these books provide state-of-the-art research summaries containing comparably advanced material so that typically further literature has to be consulted when used in a lecture. In contrast to these works, our book aims to be more comprehensive in that it also covers the foundations of music and signal analysis and introduces the required basics in the fields of statistics and data mining. Furthermore, examples based on music data are provided for all basic chapters of this book. Nonetheless, the above-mentioned books can serve as valuable additional readings for advanced topics in music data analysis.
1.3 Book Overview
General Structure The book is structured in four parts.
I “Music and Audio”: In this part we cover the basics of music in terms of the underlying physics, fundamental musical structures as well as the human perception of music. We then introduce the reader to the foundations of digital signal processing, the extraction of musical features from the audio signal and from other sources, and how to represent music in digital form.
II “Methods”: This part is devoted to statistical and machine learning methods used for music data analysis. We discuss regression, unsupervised and supervised classification, feature processing and selection, as well as methods for the evaluation of the models that result from these methods. Moreover, optimization methods that form the basis of many advanced data analysis methods are introduced.
III “Applications”: The third, and central part focuses on applications that can be based on automated music data analysis methods. The discussed applications for example include instrument, chord, and tempo recognition, the detection of emotions, music recommendation, automated composition, and the tool-supported organization of music collections.
IV “Implementation”: In the last part of the book we focus on practical considerations when building certain types of music-related applications. The topics include a case study on architectural considerations, questions of the design of user interfaces for music applications, as well as considerations of how to implement parts of a music analysis system directly on hardware.
Parts IIII mainly comprise 200 pages each, part IV comprises around 50 pages.
1.4 Chapter Summaries
Part I: Music and Audio
Chapter 2 “The Musical Signal: Physically and Psychologically”: The computerized processing of music requires some understanding of the physical and sensational aspects of musical tones. In this chapter, we discuss the key characteristics of a tone, which are its pitch, volume, timbre, and duration. For each of these “moments” we give a description based on concepts from the fields of physics, psychoacoustics and music.
Chapter 3 “Musical Structures and Their Perception”: In this chapter, the basics of musical harmonies and polyphony – the basis of Western tonal music – are reviewed. Furthermore, the concepts of consonant and dissonant intervals, which form the basis of Western music aesthetics, are discussed and the rules of tone progression and the fundamentals of the craft of counterpoint are explained. Then, the basic concept of chords as combinations of several tones and their harmonic functions are presented along with an eleme...

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Estilos de citas para Music Data Analysis

APA 6 Citation

[author missing]. (2016). Music Data Analysis (1st ed.). CRC Press. Retrieved from https://www.perlego.com/book/2029315/music-data-analysis-foundations-and-applications-pdf (Original work published 2016)

Chicago Citation

[author missing]. (2016) 2016. Music Data Analysis. 1st ed. CRC Press. https://www.perlego.com/book/2029315/music-data-analysis-foundations-and-applications-pdf.

Harvard Citation

[author missing] (2016) Music Data Analysis. 1st edn. CRC Press. Available at: https://www.perlego.com/book/2029315/music-data-analysis-foundations-and-applications-pdf (Accessed: 15 October 2022).

MLA 7 Citation

[author missing]. Music Data Analysis. 1st ed. CRC Press, 2016. Web. 15 Oct. 2022.