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Music Data Mining by Tao Li,Mitsunori Ogihara,George Tzanetakis Pdf
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
Music Data Analysis by Claus Weihs,Dietmar Jannach,Igor Vatolkin,Guenter Rudolph Pdf
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.
Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications by Wang, John Pdf
In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.
Recent Advances on Soft Computing and Data Mining by Tutut Herawan,Rozaida Ghazali,Mustafa Mat Deris Pdf
This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.
From Data to Decisions in Music Education Research by Brian C. Wesolowski Pdf
From Data to Decisions in Music Education Research provides a structured and hands-on approach to working with empirical data in the context of music education research. Using step-by-step tutorials with in-depth examples of music education data, this book draws upon concepts in data science and statistics to provide a comprehensive framework for working with a variety of data and solving data-driven problems. All of the skills presented here use the R programming language, a free, open-source statistical computing and graphics environment. Using R enables readers to refine their computational thinking abilities and data literacy skills while facilitating reproducibility, replication, and transparency of data analysis in the field. The book offers: A clear and comprehensive framework for thinking about data analysis processes in a music education context. An overview of common data structures and data types used in statistical programming and data analytics. Techniques for cleaning, preprocessing, manipulating, aggregating, and mining data in ways that facilitate organization and interpretation. Methods for summarizing and visualizing data to help identify structures, patterns, and trends within data sets. Detailed applications of descriptive, diagnostic, and predictive analytics processes. Step-by-step code for all concepts and analyses. Direct access to all data sets and R script files through the accompanying eResource. From Data to Decisions in Music Education Research offers a reference "cookbook" of code and programming recipes written with the graduate music education student in mind and breaks down data analysis processes and skills in an approachable fashion. It can be used across a wide range of graduate music education courses that rely on the application of empirical data analyses and will be useful to all music education scholars and professionals seeking to enhance their use of quantitative data.
Advanced Data Mining and Applications by Xue Li,Osmar R. Zaiane,Zhanhuai Li Pdf
Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.
Web Mining by V.S. Kumbhar,K. S. Oza,R.K. Kamat Pdf
Web mining is the application of data mining strategies to excerpt learning from web information, i.e. web content, web structure, and web usage data. With the emergence of the web as the predominant and converging platform for communication, business and scholastic information dissemination, especially in the last five years, there are ever increasing research groups working on different aspects of web mining mainly in three directions. These are: mining of web content, web structure and web usage. In this context there are good number of frameworks and benchmarks related to the metrics of the websites which is certainly weighty for B2B, B2C and in general in any e-commerce paradigm. Owing to the popularity of this topic there are few books in the market, dealing more on such performance metrics and other related issues. This book, however, omits all such routine topics and lays more emphasis on the classification and clustering aspects of the websites in order to come out with the true perception of the websites in light of its usability.In nutshell, Web Mining: A Synergic Approach Resorting to Classifications and Clustering showcases an effective methodology for classification and clustering of web sites from their usability point of view. While the clustering and classification is accomplished by using an open source tool WEKA, the basic dataset for the selected websites has been emanated by using a free tool site-analyzer. As a case study, several commercial websites have been analyzed. The dataset preparation using site-analyzer and classification through WEKA by embedding different algorithms is one of the unique selling points of this book. This text projects a complete spectrum of web mining from its very inception through data mining and takes the reader up to the application level. Salient features of the book include: Literature review of research work in the area of web miningBusiness websites domain researched, and data collected using site-analyzer toolAccessibility, design, text, multimedia, and networking are assessedDatasets are filtered further by selecting vital attributes which are Search Engine Optimized for processing using the Weka attributed toolDataset with labels have been classified using J48, RBFNetwork, NaïveBayes, and SMO techniques using WekaA comparative analysis of all classifiers is reportedCommercial applications for improving website performance based on SEO is given
Processing and Managing Complex Data for Decision Support by Darmont, Jrme,Boussaid, Omar Pdf
"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.
Database Technologies: Concepts, Methodologies, Tools, and Applications by Erickson, John Pdf
"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.
INFORMATION TECHNOLOGY : THEORY AND PRACTICE by SINHA, PRADEEP K. ,SINHA, PRITI Pdf
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.
Data Mining and Big Data by Ying Tan,Yuhui Shi Pdf
This book constitutes the refereed proceedings of the 4th International Conference on Data Mining and Big Data, DMBD 2019, held in Chiang Mai, Thailand, in July 2019. The 26 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. They are organized in topical sections named: data analysis; prediction; clustering; classification; mining pattern; mining tasks.
Handbook of Multimedia for Digital Entertainment and Arts by Borko Furht Pdf
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.
Machine Learning and Music Generation by José M. Iñesta,Darrell C. Conklin,Rafael Ramírez-Melendez,Thomas M. Fiore Pdf
Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.
Data Mining: Concepts and Techniques by Jiawei Han,Micheline Kamber,Jian Pei Pdf
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Machine Learning and Music Generation by José M. Iñesta,Darrell C. Conklin,Rafael Ramírez-Melendez,Thomas M. Fiore Pdf
Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.