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 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 entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage – the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston – two of our finest scholars in the area of entertainment marketing – have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can’t be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Kölmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science’s winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allègre Hadida, Associate Professor in Strategy, University of Cambridge

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.

In recent years, academics and professionals in the social sciences have forged significant advances in quantitative research methodologies specific to their respective disciplines. Although new and sophisticated techniques for large-scale data analyses have become commonplace in general educational, psychological, sociological, and econometric fields, many researchers in music education have yet to be exposed to such techniques. Design and Analysis of Quantitative Research in Music Education is a comprehensive reference for those involved with research in music education and related fields, providing a foundational understanding of quantitative inquiry methods. Authors Peter Miksza and Kenneth Elpus update and expand the set of resources that music researchers have at their disposal for conceptualizing and analyzing data pertaining to music-related phenomena. This text is designed to familiarize readers with foundational issues of quantitative inquiry as a point of view, introduce and elaborate upon issues of fundamental quantitative research design and analysis, and expose researchers to new, innovative, and exciting methods for dealing with complex research questions and analyzing large samples of data in a rigorous and thorough manner. With this resource, researchers will be better equipped for dealing with the challenges of the increasingly information-rich and data-driven environment surrounding music education. An accompanying companion website provides valuable supplementary exercises and videos.

Help Penelope figure out how to solve the puzzle and make sure all the guests at her birthday party can enjoy their favorite slice of pizza pie! Penelope prefers plain, but Barnaby Barchart, Laney Line, and Bertie Boxplot all have a favorite topping. Penelope Pie's Pizza Party is the first book in the Vizkidz series. Delving into these stories, kids will learn how to compare data sets in a bar chart, that pie charts best illustrate parts of a whole, that correlation does not equal causation, and other valuable lessons about the fundamentals of data analytics. Data collection and analysis has become core to almost every industry and function in society today, but data literacy is lagging behind. The amount of information we process every day can be of massive value, but only if we are able to keep up. Through their fun-filled adventures and number-crunching challenges our Vizkidz help young readers explore the fundamentals of data analytics and computational thinking. In a STEM-centric digital world, these are skills kids definitely need for the future.

In Statistics in Music Education Research, author Joshua Russell explains the process of using a range of statistical analyses from inception to research design to data entry to final analysis using understandable descriptions and examples from extant music education research. He explores four main aspects of music education research: understanding logical concepts of statistical procedures and their outcomes; critiquing the use of different procedures in extant and developing research; applying the correct statistical model for not only any given dataset, but also the correct logic determining which model to employ; and reporting the results of a given statistical procedure clearly and in a way that provides adequate information for the reader to determine if the data analysis is accurate and interpretable. While it is written predominately for graduate students in music education courses, Statistics in Music Education Research will also help music education researchers and teachers of music educators gain a better understanding of how parametric statistics are employed and interpreted in music education.

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to

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.

Exploring the application of Bayesian probabilistic modeling techniques to musical issues, including the perception of key and meter.