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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.
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 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.
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.
State-of-the-art coverage of modern computational methods for the analysis and design of beams Analysis and Design of Elastic Beams presents computer models and applications related to thin-walled beams such as those used in mechanical and aerospace designs, where thin, lightweight structures with high strength are needed. This book will enable readers to compute the cross-sectional properties of individual beams with arbitrary cross-sectional shapes, to apply a general-purpose computer analysis of a complete structure to determine the forces and moments in the individual members, and to use a unified approach for calculating the normal and shear stresses, as well as deflections, for those members' cross sections. In addition, this book augments a solid foundation in the basic structural design theory of beams by: * Providing coverage of thin-wall structure analysis and optimization techniques * Applying computer numerical methods to classical design methods * Developing computational solutions for cross-sectional properties and stresses using finite element analyses Including access to an associated Web site with software for the analysis and design of any cross-sectional shape, Analysis and Design of Elastic Beams: Computational Methods is an essential reference for mechanical, aerospace, and civil engineers and designers working in the automotive, ship, and aerospace industries in product and process design, machine design, structural design, and design optimization, as well as students and researchers in these areas.
These proceedings consist of 19 papers, which have been peer-reviewed by international program committee and selected for the 5th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2017), which was held on June 30–July 1, 2017 in Berlin, Germany. The respective chapters discuss both theoretical and practical issues in connection with computational methods and optimization methods for knowledge engineering. The broad range of application areas discussed includes network computing, simulation, intelligent and adaptive e-learning, information retrieval, sentiment analysis, autonomous underwater vehicles, social media analysis, natural language processing, biomimetics in organizations, and cash management. In addition to pure content, the book offers many inspiring ideas and suggests new research directions, making it a valuable resource for graduate students, Ph.D. students, and researchers in Computer Science and Applied Mathematics alike.
The idea of this monograph is to present an overview of decisive theoretical, computational, technological, aesthetical, artistic, economical, and sociological directions to create future music. It features a unique insight into dominant scientific and artistic new directions, which are guaranteed by the authors' prominent publications in books, software, musical, and dance productions. Applying recent research results from mathematical and computational music theory and software as well as new ideas of embodiment approaches and non-Western music cultures, this book presents new composition methods and technologies. Mathematical, computational, and semiotic models of artistic presence (imaginary time, gestural creativity) as well as strategies are also covered. This book will be of interest to composers, music technicians, and organizers in the internet-based music industry, who are offered concrete conceptual architectures and tools for their future strategies in musical creativity and production.
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.
This book constitutes the refereed proceedings of the Second International Conference on Music and Artificial Intelligence, ICMAI 2002, held in Edinburgh, Scotland, UK in September 2002.The 16 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected for inclusion in the proceedings. Among the topics addressed are parsing for music and language, patterns in music, musical pattern recognition, visualisation, sound classification, tonal structure representation, musical learning systems, pattern analysis, musical perception, melodic segmentation, and time series analysis.
This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001. The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.