Music Data Mining (chapman & Hall/crc Data Mining And Knowledge Discovery Series)
by Mitsunori Ogihara /
2011 / English / PDF
5.8 MB Download
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,
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
Music Data
Mining presents a variety of approaches to successfully
employ data mining techniques for the purpose of music
processing.
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 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 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.