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 constitutes the refereed proceedings of the Third International Conference on Mathematics and Computation in Music, MCM 2011, held in Paris, France, in June 2011. The 24 revised full papers presented and the 12 short papers were carefully reviewed and selected from 62 submissions. The MCM conference is the flagship conference of the Society for Mathematics and Computation in Music. This year’s conference aimed to provide a multi-disciplinary platform dedicated to the communication and exchange of ideas amongst researchers involved in mathematics, computer science, music theory, composition, musicology, or other related disciplines. Areas covered were formalization and geometrical representation of musical structures and processes; mathematical models for music improvisation and gestures theory; set-theoretical and transformational approaches; computational analysis and cognitive musicology as well as more general discussions on history, philosophy and epistemology of music and mathematics.

The interest of AI in problems related to understanding sounds has a rich history dating back to the ARPA Speech Understanding Project in the 1970s. While a great deal has been learned from this and subsequent speech understanding research, the goal of building systems that can understand general acoustic signals--continuous speech and/or non-speech sounds--from unconstrained environments is still unrealized. Instead, there are now systems that understand "clean" speech well in relatively noiseless laboratory environments, but that break down in more realistic, noisier environments. As seen in the "cocktail-party effect," humans and other mammals have the ability to selectively attend to sound from a particular source, even when it is mixed with other sounds. Computers also need to be able to decide which parts of a mixed acoustic signal are relevant to a particular purpose--which part should be interpreted as speech, and which should be interpreted as a door closing, an air conditioner humming, or another person interrupting. Observations such as these have led a number of researchers to conclude that research on speech understanding and on nonspeech understanding need to be united within a more general framework. Researchers have also begun trying to understand computational auditory frameworks as parts of larger perception systems whose purpose is to give a computer integrated information about the real world. Inspiration for this work ranges from research on how different sensors can be integrated to models of how humans' auditory apparatus works in concert with vision, proprioception, etc. Representing some of the most advanced work on computers understanding speech, this collection of papers covers the work being done to integrate speech and nonspeech understanding in computer systems.

This book constitutes the thoroughly refereed proceedings of the 5th International Conference on Mathematics and Computation in Music, MCM 2015, held in London, UK, in June 2015. The 24 full papers and 14 short papers presented were carefully reviewed and selected from 64 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in topical sections on notation and representation, music generation, patterns, performance, similarity and contrast, post-tonal music analysis, geometric approaches, deep learning, and scales.

This definitive reference resource examines how music affects human beings and their interactions in and with the world. The interdisciplinary nature of the work provides a starting place for students to situate the status of music within the social sciences in fields such as anthropology, communications, psychology, linguistics, sociology, sports, political science and economics, as well as biology and the health sciences. Features: Approximately 450 articles, arranged in A-to-Z fashion and richly illustrated with photographs, provide the social and behavioral context for examining the importance of music in society. Entries are authored and signed by experts in the field and conclude with references and further readings, as well as cross references to related entries. A Reader′s Guide groups related entries by broad topic areas and themes, making it easy for readers to quickly identify related entries. A Chronology of Music places material into historical context; a Glossary defines key terms from the field; and a Resource Guide provides lists of books, academic journals, websites and cross-references. The multimedia digital edition is enhanced with video and audio clips and features strong search-and-browse capabilities through the electronic Reader’s Guide, detailed index, and cross references. Music in the Social and Behavioral Sciences, available in both multimedia digital and print formats, is a must-have reference for music and social science library collections. Key Themes: Aesthetics and Emotion Business and Technology Communities and Society Culture and Environment Elements of Musical Examination Evolutionary Psychology Media and Communication Musicianship and Expertise Neuroscience Perception, Memory, Cognition Politics, Economics, Law Therapy, Health, Wellbeing

A state-of-the-art overview of the analysis of electroacoustic music, which includes discussions of a wide range of works.

Lecture Series on Computer and on Computational Sciences (LSCCS) aims to provide a medium for the publication of new results and developments of high-level research and education in the field of computer and computational science.In this series, only selected proceedings of conferences in all areas of computer science and computational sciences wil

This edited monograph provides a compelling analysis of the interplay between neuroscience and aesthetics. The book broaches a wide spectrum of topics including, but not limited to, mathematics and creator algorithms, neurosciences of artistic creativity, paintings and dynamical systems as well as computational research for architecture. The international authorship is genuinely interdisciplinary and the target audience primarily comprises readers interested in transdisciplinary research between neuroscience and the broad field of aesthetics.

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.

The Geometry of Musical Rhythm: What Makes a "Good" Rhythm Good? is the first book to provide a systematic and accessible computational geometric analysis of the musical rhythms of the world. It explains how the study of the mathematical properties of musical rhythm generates common mathematical problems that arise in a variety of seemingly disparate fields. For the music community, the book also introduces the distance approach to phylogenetic analysis and illustrates its application to the study of musical rhythm. Accessible to both academics and musicians, the text requires a minimal set of prerequisites. Emphasizing a visual geometric treatment of musical rhythm and its underlying structures, the author—an eminent computer scientist and music theory researcher—presents new symbolic geometric approaches and often compares them to existing methods. He shows how distance geometry and phylogenetic analysis can be used in comparative musicology, ethnomusicology, and evolutionary musicology research. The book also strengthens the bridge between these disciplines and mathematical music theory. Many concepts are illustrated with examples using a group of six distinguished rhythms that feature prominently in world music, including the clave son. Exploring the mathematical properties of good rhythms, this book offers an original computational geometric approach for analyzing musical rhythm and its underlying structures. With numerous figures to complement the explanations, it is suitable for a wide audience, from musicians, composers, and electronic music programmers to music theorists and psychologists to computer scientists and mathematicians. It can also be used in an undergraduate course on music technology, music and computers, or music and mathematics.