A mood-based music classification and exploration system
Name
173521043-MIT.pdf
Description
Full printable version
Size
7.21 MB
Format
Adobe PDF
Checksum (MD5)
7b599b705806e1e010c2147f67f491ab
Author(s)
Meyers, Owen Craigie
Advisor(s)
Barry Vercoe.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.
Includes bibliographical references (p. 89-93).
Subjects
Architecture. Program In Media Arts and Sciences
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Persistent DSpace Link
