Tao Li – författare

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 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.

RFID (radio frequency identification) tags are becoming ubiquitously available in object tracking, access control, and toll payment. The current application model treats tags simply as ID carriers and deals with each tag individually for the purpose of identifying the object that the tag is attached to. The uniqueness of RFID as an Infrastructure is to change the traditional individual view to a collective view that treats universally-deployed tags as a new infrastructure, a new wireless platform on which novel applications can be developed.

 The book begins with an introduction to the problems of tag estimation and information collection from RFID systems, and explains the challenges. It discusses how to efficiently estimate the number of tags in a large RFID system, considering both energy cost and execution time. It then gives a detailed account on how to collect information from a sensor-augmented RFID network with new designs that significantly reduce execution time.

This book constitutes the proceedings of the 13th International Symposium on Advanced Parallel Processing Technologies, APPT 2019, held in Tianjin, China, in August 2019.The 11 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections named: System Support for Neural Networks; Scheduling and File Systems; Optimization and Parallelization; Security and Algorithms.

This two volume set LNCS 9418 and LNCS 9419 constitutes the proceedings of the 16th International Conference on Web Information Systems Engineering, WISE 2015, held in Miami, FL, USA, in November 2015. The 53 full papers, 17 short and 14 special sessions and invited papers, presented in these proceedings were carefully reviewed and selected from 189 submissions. The papers cover the areas of big data techniques and applications, deep/hidden web, integration of web and internet, linked open data, semantic web, social network computing, social web and applications, social web models, analysis and mining, web-based applications, web-based business processes and web services, web data integration and mashups, web data models, web information retrieval, web privacy and security, web-based recommendations, and web search.

This two volume set LNCS 9418 and LNCS 9419 constitutesthe proceedings of the 16th International Conference on Web Information SystemsEngineering, WISE 2015, held in Miami, FL, USA, in November 2015.

The 53 full papers, 17 short and 14 special sessions andinvited papers, presented in these proceedings were carefully reviewed andselected from 189 submissions. The papers cover the areas of big datatechniques and applications, deep/hidden Web, integration of Web and internet,linked open data, semantic Web, social network computing, social Web andapplications, social Web models, analysis and mining, Web-based applications,Web-based business processes and Web services, Web data integration andmashups, Web data models, Web information retrieval, Web privacy and security,Web-based recommendations, and Web search.