This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmonic and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally, classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry.

The book opens with a short introduction to Indian music, in particular classical Hindustani music, followed by a chapter on the role of statistics in computational musicology. The authors then show how to analyze musical structure using Rubato, the music software package for statistical analysis, in particular addressing modeling, melodic similarity and lengths, and entropy analysis; they then show how to analyze musical performance. Finally, they explain how the concept of seminatural composition can help a music composer to obtain the opening line of a raga-based song using Monte Carlo simulation. The book will be of interest to musicians and musicologists, particularly those engaged with Indian music.

Music is an art form which is realized in time. This dissertation presents computational methods for examining the temporality of music at multiple time-scales so that both short-term surface features and deeper long-term structures can be studied and related to each other. The methods are applied in particular to musical key analysis (Chapters 2-4) and also adapted for use in performance analysis (Chapters 5-6). The essential methodology is to examine all sequential time-scales within a piece using some analytic process and then arrange a summary of the analytic results into a maximally overlapped arrangement. Chapter 2 defines a two-dimensional plotting domain for displaying musical features at all possible time-scales which forms a basis for further analysis methods. The resulting structures in the plots can be examined subjectively as a navigational aid in the music as illustrated in Chapters 3 and 5. They can also be used to extract musically relevant information as discussed in Chapters 4 and 6.

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover a wide range of topics and application areas, including: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.

This book explains the state of the art in the use of the discrete Fourier transform (DFT) of musical structures such as rhythms or scales. In particular the author explains the DFT of pitch-class distributions, homometry and the phase retrieval problem, nil Fourier coefficients and tilings, saliency, extrapolation to the continuous Fourier transform and continuous spaces, and the meaning of the phases of Fourier coefficients. This is the first textbook dedicated to this subject, and with supporting examples and exercises this is suitable for researchers and advanced undergraduate and graduate students of music, computer science and engineering. The author has made online supplementary material available, and the book is also suitable for practitioners who want to learn about techniques for understanding musical notions and who want to gain musical insights into mathematical problems.

Today's computers provide music theorists with unprecedented opportunities to analyze music more quickly and accurately than ever before. Where analysis once required several weeks or even months to complete¿often replete with human errors, computers now provide the means to accomplish these same analyses in a fraction of the time and with far more accuracy. However, while such computer music analyses represent significant improvements in the field, computational analyses using traditional approaches by themselves do not constitute the true innovations in music theory that computers offer. In Hidden Structure: Music Analysis Using Computers David Cope introduces a series of analytical processes that¿by virtue of their concept and design¿can be better, and in some cases, only accomplished by computer programs, thereby presenting unique opportunities for music theorists to understand more thoroughly the various kinds of music they study.Following the introductory chapter that covers several important premises, Hidden Structure focuses on several unique approaches to music analysis offered by computer programs. While these unique approaches do not represent an all-encompassing and integrated global theory of music analysis, they do represent significantly more than a compilation of loosely related computer program descriptions. For example, Chapter 5 on function in post-tonal music, firmly depends on the scalar foundations presented in chapter 4. Likewise, chapter 7 presents a multi-tiered approach to musical analysis that builds on the material found in all of the preceding chapters. In short, Hidden Structure uniquely offers an integrated view of computer music analysis for today¿s musicians.

This volume brings together a group of analytical chapters exploring traditional genres and styles of world music, capturing a vibrant and expanding field of research. These contributors, drawn from the forefront of researchers in world music analysis, seek to break down barriers and build bridges between scholarly disciplines, musical repertoires, and cultural traditions. Covering a wide range of genres, styles, and performers, the chapters bring to bear a variety of methodologies, including indigenous theoretical perspectives, Western music theory, and interdisciplinary techniques rooted in the cognitive and computational sciences. With contributors addressing music traditions from Africa, Asia, Europe, and the Americas, this volume captures the many current directions in the analysis of world music, offering a state of the fi eld and demonstrating the expansion of possibilities created by this area of research.