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 successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.

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

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site

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 an overall view of the emerging field of complex data processing, highlighting the similarities between the different data, issues and approaches"--Provided by publisher.

"This reference expands the field of database technologies through four-volumes of in-depth, advanced research articles from nearly 300 of the world's leading professionals"--Provided by publisher.

This book is based on the premise that knowledge of Information Technology (IT) is essential today for people in every walk of life and all types of profession. It is designed to impart a unified body of knowledge and practice in IT to its readers. Readers can apply this knowledge in innovative ways for various strategic advantages such as increasing productivity, improving quality of products and services, problem solving, decision making, and improving their own and others living standards. The textbook takes a practical approach to introduce the various components of IT to its readers. While doing so, it demonstrates how IT is being used in modern enterprises by various departments to carry out their activities with greater ease, speed, and accuracy than before. It also introduces several new business models and practices made possible due to IT that enterprises are now using for better profitability. In the process, the book provides to its readers a sound foundation of various components and aspects of IT. It also introduces to its readers several latest concepts and technologies in IT such as Wearable computers, Green computing, Cloud computing, Speech recognition and voice response systems, 4G and 5G networks, Big data analytics, Data science, Web 3.0, IPv6, 3D printing, Enterprise 2.0 organization, etc.

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 advances in computer entertainment, multi-player and online games, technology-enabled art, culture and performance have created a new form of entertainment and art. The success of this new field has influenced the development of the digital entertainment industry and related products/services, which has impacted every aspect of our lives. Handbook of Multimedia for Digital Entertainment and Arts is an edited volume contributed by worldwide experts in the field of the new digital and interactive media, and their applications in entertainment and arts. This handbook covers leading edge media technologies, and the latest research applied to digital entertainment and arts. The main focus of Handbook of Multimedia for Digital Entertainment and Arts targets interactive and online games, edutainment, e-performance, personal broadcasting, innovative technologies for digital arts, digital visual and auditory media, augmented reality, moving media, and other advanced topics. The final chapters of this book present future trends and developments within this explosive field. Handbook of Multimedia for Digital Entertainment and Arts serves as a primary reference for advanced-level students, researchers and professors studying computer science and electrical engineering. With the dramatic growth of interactive digital entertainment and art applications, this handbook is also suitable as a reference for practitioners, programmers, and engineers working in this field.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms