Music Data Mining by Tao Li
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.
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Data Mining with R
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Biological Data Mining
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Exploratory Data Analysis Using R
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Advanced Data Science and Analytics with Python
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Automated Data Analysis Using Excel
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Privacy-Aware Knowledge Discovery
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Computational Intelligent Data Analysis for Sustainable Development
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Data Mining for Design and Marketing
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Data Classification
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Statistical Data Mining Using SAS Applications
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Mining Software Specifications
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Contrast Data Mining
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Introduction to Computational Health Informatics
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Social Networks with Rich Edge Semantics
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Graph-Based Social Media Analysis
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Industrial Applications of Machine Learning
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Computational Business Analytics
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Event Mining
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Advances in Machine Learning and Data Mining for Astronomy
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Human Capital Systems, Analytics, and Data Mining
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Practical Graph Mining with R
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Large-Scale Machine Learning in the Earth Sciences
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Support Vector Machines
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Geographic Data Mining and Knowledge Discovery
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Demystifying AI
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Data Science and Machine Learning for Non-Programmers
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Knowledge Discovery from Data Streams
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Data Science and Analytics with Python
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RapidMiner, Second Edition
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Feature Engineering for Machine Learning and Data Analytics
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Text Mining and Visualization
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Knowledge Guided Machine Learning
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Healthcare Data Analytics
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Text Mining
"… a useful survey for the reader specifically interested in MIR"
—Statistical Papers (2013) 54
"This book, as a collection of papers, brings together some of the leading scholars of the field to tackle a number of data mining techniques aiming mainly at data classification."
—Joonas Kauppinen, International Statistical Review, 2012
| SKU | Unavailable |
| ISBN 13 | 9781439835524 |
| ISBN 10 | 1439835527 |
| Title | Music Data Mining |
| Author | Tao Li |
| Series | Chapman And Hall Crc Data Mining And Knowledge Discovery Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Taylor & Francis Inc |
| Year published | 2011-07-12 |
| Number of pages | 384 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |