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This book discusses the applications of evolutionary computation to music and the tools needed to create and study such systems. These tools can be combined to create surrogate artificial worlds populated by interacting simulated organisms in which complex musical experiments can be performed. The book demonstrates that evolutionary systems can be used to create and to study musical compositions and cultures in ways that have never before been achieved.
This book is a printed edition of the Special Issue "Sound and Music Computing" that was published in Applied Sciences
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.
This book presents a coherent state-of-the-art survey on the area of systematic and cognitive musicology which has enjoyed dynamic growth now for many years. It is devoted to exploring the relationships between acoustics, human information processing, and culture as well as to methodological issues raised by the widespread use of computers as a powerful tool for theory construction, theory testing, and the manipulation of musical information or any kind of data manipulation related to music.
Virtual Music is about artificial creativity. Focusing on the author's Experiments in Musical Intelligence computer music composing program, the author and a distinguished group of experts discuss many of the issues surrounding the program, including artificial intelligence, music cognition, and aesthetics. The book is divided into four parts. The first part provides a historical background to Experiments in Musical Intelligence, including examples of historical antecedents, followed by an overview of the program by Douglas Hofstadter. The second part follows the composition of an Experiments in Musical Intelligence work, from the creation of a database to the completion of a new work in the style of Mozart. It includes, in sophisticated lay terms, relatively detailed explanations of how each step in the process contributes to the final composition. The third part consists of perspectives and analyses by Jonathan Berger, Daniel Dennett, Bernard Greenberg, Douglas R. Hofstadter, Steve Larson, and Eleanor Selfridge-Field. The fourth part presents the author's responses to these commentaries, as well as his thoughts on the implications of artificial creativity. The book (and corresponding Web site) includes an appendix providing extended musical examples referred to and discussed in the book, including composers such as Scarlatti, Bach, Mozart, Beethoven, Schubert, Chopin, Puccini, Rachmaninoff, Prokofiev, Debussy, Bartok, and others. It is also accompanied by a CD containing performances of the music in the text.
This textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval. Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, computer science, multimedia, and musicology. The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform—concepts that are then used throughout the book. In the subsequent chapters, concrete music processing tasks serve as a starting point. Each of these chapters is organized in a similar fashion and starts with a general description of the music processing scenario at hand before integrating it into a wider context. It then discusses—in a mathematically rigorous way—important techniques and algorithms that are generally applicable to a wide range of analysis, classification, and retrieval problems. At the same time, the techniques are directly applied to a specific music processing task. By mixing theory and practice, the book’s goal is to offer detailed technological insights as well as a deep understanding of music processing applications. Each chapter ends with a section that includes links to the research literature, suggestions for further reading, a list of references, and exercises. The chapters are organized in a modular fashion, thus offering lecturers and readers many ways to choose, rearrange or supplement the material. Accordingly, selected chapters or individual sections can easily be integrated into courses on general multimedia, information science, signal processing, music informatics, or the digital humanities.
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.
The first of its kind, this is book consists of twenty-one essays describing the many different uses of the digital computer in the field of music. Musicologists will find that various historical periods-from medieval to contemporary-are represented, and examples of computer analysis of ethnic music are considered. Edmund A. Bowles contributes an entertaining historical survey of music research and the computer. Lejaren Hill here discusses computer composition, both in this country and in Europe, and gives a bibliography of composers and their works. A. James Gabura's essay describes experiments in analyzing and identifying the keyboard styles of Haydn, Mozart, and Beethoven. There is also a section of particular interest to music librarians.
Recent studies from Europe and North America concerned with the query and analysis of musical data.The field of music query has grown from tentative beginnings in bibliographical systems of earlier decades to a substantial area of interdisciplinary studies in little more than a decade. This volume assembles recent studies from Europe and North America concerned with the query and analysis of musical data. Among these, methods for the synchronization of sound and symbolic data, for automatic analysis through perceptual rules, and for computing a transportation distance for thematic comparison are described. The modeling of rhythmic motifs, of melodic traits, and of cognitive distance are discussed. User studies report on human preferences in modes of query (humming vs. tapping, etc.) and on the comparative success rates of more than two dozen proposed metrics for melodic comparison.
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.