MUSIC DATA MINING by Tao Li (Editor), Mitsunori Ogihara (Editor), George Tzanetakis (Editor) ( 9781439835524 )

Item Details

Title:

MUSIC DATA MINING

By:

Tao Li (Editor), Mitsunori Ogihara (Editor), George Tzanetakis (Editor)

Format:

Hardback

List price:

£105.00

Our price:

£94.50

Discount:

10% off

You save:

£10.50

ISBN 10:

1439835527

ISBN 13:

9781439835524

Availability:

Usually dispatched within 1-3 weeks.

Delivery rates

Stock:

Currently 0 available

Publisher:

TAYLOR & FRANCIS INC

Pub. date:

12 July, 2011

Series:

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Pages:

384

Description:

With most people now obtaining music digitally, challenges have arisen in the storage and organisation of large digital music collections. This book explores how data mining addresses these challenges by mining useful information and using it to create novel ways of interaction with large music collections.

Synopsis:

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.

Illustrations:

64 black & white illustrations, 42 black & white tables

Publication:

Imprint:

CRC Press Inc

Returns:

Returnable

Some other items by this author:

CANTONESE (PB)

EVENT MINING

EVENT MINING (HB)

EXPLORING DATA SCIENCE WITH R AND THE TIDYVERSE