Social Tags and Music Information Retrieval (Part II)
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Transcript of Social Tags and Music Information Retrieval (Part II)
Social Tags and
Music Information Retrieval
Part II
ISMIR 2008
Paul Lamere Sun Microsystems Inc.
Elias Pampalk Last.fm
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags (continued)
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Other sources of tags
Autotagging
Uses content analysis to automatically apply tags
Tags acquired from other sources (social tags, games, web crawling) can be 'learned'
New music or unpopular music can be autotagged with the 'learned' tags.
Can scale to the long tail
Time per million songs:
Manual: with 100 people = 3 Years
Automatic:with 100 CPUs = 8 Hours
Cost per million songs
Manual: ~ $10,000,000
Automatic: ~ $100
Other sources of tags: Autotagging
How it works
Labeled Examples
Unknown Examples
Machine Learning
Model
Labeled Examples
WindowingMEL ScaleDecodeFFTLogDCTMFCCFeature Extraction
Training
Tagging
Other sources of tags: Autotagging
Ground Truth
Standard (genre) classification
each item: 1 category/class/tag
annotated by a trusted expert
Social tags
each item: unlimited (weighted) categories/class/tag
annotated by an anonymous crowd
Other sources of tags: Autotagging
Ground Truth for Classifiers
[Craft, Wiggens, Crawford, ISMIR 2007]
Other sources of tags: Autotagging
Ground Truth for Classifiers
[Paul Lamere, JNMR 2008]
Autotagging: Ground truth & evaluation
MIREX 2008: Tag Track
Data
MajorMiner game (Mandel & Ellis)
2300 audio clips (10 second) from 1400 tracks from 500 artists (artist-filtering used to ensure training and test don't have different sets of artists)
43 tags verified at least 35 times each
(drums, guitar, male, rock, ...)
Autotagging: Ground truth & evaluation
MIREX 2008: Tag Track
Tasks
Clip tag (yes/no)
Clip list of tags (ranked)
Tag list of clips (ranked)
Standardized evaluation procedures
Statistical significance etc
http://www.music-ir.org/mirex/2008/index.php/Audio_Tag_Classification
Autotagging
LabROSA
Features: MFCCs + temporal features
Learning: Support Vector Machine with a radial basis function kernel
Compared ability to learn 'game' tags vs. social tags
M. I. Mandel and D. P. W. Ellis. A Web-Based Game
for Collecting Music Metadata. In Journal of New Music Research,
2008 (to appear).
http://majorminer.com/search
Other sources of tags: Autotagging
LabROSA
T. Bertin-Mahiex, D. Eck, F. Maillet, and P. Lamere. Autotagger:
A model for
predicting social tags from acoustic features on large music
databases.
In Journal of New Music Research, 2008 (to appear).
Other sources of tags: Autotagging
BRAMS, Sun Labs
[Bertin-Mahiex et al. JNMR 2008]
Features: MFCCs + Temporal
Learning: AdaBoost and FilterBoost
Medium scale: 100,000 tracks
"Large and Noise" vs. "Small and Clean"
There's no data like more data
Other sources of tags: Autotagging
BRAMS, Sun Labs
Other sources of tags: Autotagging
BRAMS, Sun Labs
D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet.
Towards musical query-by-semantic description using the CAL500 data
set. SIGIR 2007.
Other sources of tags: Autotagging
CAL UCSD
Other sources of tags: Autotagging
Challenges
Not all tags are can be easily derived from audio
Examples: seen live, great lyrics, awesome, crap, Boston, Montreal, UK
Can we identify words that are musically meaningful?
Some tags may be too subtle for current systems to distinguish
Power Metal vs. Speed Metal
Grunge vs. Post-Grunge
Dealing with co-occurring tags
Dealing with weak labeling not every song with piano is labeled piano
Scale
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Search & Discovery
Search & Discovery
The Vocabulary Problem
When searching people will often use the wrong words.
Should I search for "rnb", "r and b", "r&b", or "rnb" ?
With well tagged items it doesn't matter
http://www.last.fm/music/Rihanna/+tags
Search & Discovery
The Vocabulary Problem
What about infrequently tagged items?
We can use overall tag overlap to infer synonymy
Cluster tags
via overlap, tf-idf or other similarity metric
Augment query with synonyms
Not just for synonyms
can deal with spelling errors (rithm and blues)
can help with multi-lingual tags
The Vocabulary Problem
Using tag clustering to combat synonymy
The 'female' cluster of a 2,000 node tag hierarchy
The Vocabulary Problem
Multiple languages
Similar tags to
deutscher hiphop
German hip-hop
Deutscher Hip Hop
Hamburg
Eimbush
Deutschrap
Deutsch
Deutschsprachig
Hiphop
German rap
German
The Vocabulary Problem
Using item clustering to combat polysemy
The Vocabulary Problem
Using Latent Semantic Analysis
FemaleSinging
Unstructured/Unreliable representation
Latent Semantic Model
Can address:
Synonym, Polysemy
Noise
Dimensionality reduction
Latent Semantic Analysis
Query
Latent Semantic Space
Girl, GirlBand,
Grrl, Girly
Female SingerFemaleFemale Vocalists
Diva
Chicks
Woman
Pop
American IdolPop
RnBRhythm & Blues
GirlPop
Female PopSearch
LSA
Project query into semantic space
Best match may not have any tags that match the query!
See: Levy & Sandler, Learning Latent Semantic Models for Music from Social Tags. JNMR 2008.
Similarity of text
Weight the terms
Some terms are more meaningful than others: rock vs. shoegaze
TF x IDF
Distance between term vectors
Cosine distance
the cosine of the angle between the two vectors
Independent of length of vectors
Search, Discovery & Recommendation
Item Similarity
Artist similarity based on tags for Weezer
Top Tags
Alternative
Rock
Indie
Punk
Pop
Power pop
Geek Rock
90s
Metal
Indie pop
Distinctive Tags
Geek Rock
Punk-pop
College Rock
Not Emo
Overrated
Los Angeles
Modern Rock
Pop punk
California
Pop Rock
Similar Artists via Tags
Green Day
Phantom Planet
The Offspring
Sugarcult
Foo Fighters
blink-182
Ozma
Jimmy Eat World
Nada Surf
The Ataris
Similar Artists via CF
The White Stripes
Foo Fighters
Death Cab For Cutie
Beck
Radiohead
Green Day
Coldplay
The Beatles
The Killers
The Smashing Pumpkins
Web survey: Tag-based artist similarity scores better than CF-based similarity
Search, Discovery & Recommendation
Artist Similarity
Tag similarity based on artists
Metal
Metallica
System of a down
Iron Maiden
Rammstein
Slipknot
In Flames
Korn
Pantera
Judas Priest
Heavy Metal
Iron Maiden
Judas Priest
Black Sabbath
Manowar
Motorhead
Pantera
Megadeth
Ozzy Osbourne
Dio
Pop
Madonna
The Beatles
Black Eyed Peas
Beach Boys
Kelly Clarkson
Michael Jackson
Gwen Stefani
Coldplay
U2
Search, Discovery & Recommendation
Tag Similarity
Tag similarity examples
Similar tags to relax
Relaxing
Calm
Chill
Meditation
Spiritual
Chill out
Soft
Dreamy
New age
Mellow
Search, Discovery & Recommendation
Tag Similarity
Similar tags to
deutscher hiphop
German hip-hop
Deutscher Hip Hop
Hamburg
Eimbush
Deutschrap
Deutsch
Deutschsprachig
Hiphop
German rap
German
Similar tags to
turntablism
dj
abstract hip-hop
scratch
instrumental hip-hop
beats
ninja tune
ambient breakbeat
hip hop
instrumental hip hop
underground hip hop
Similar tags to
metal
heavy metal
death metal
thrash metal
hard rock
progressive metal
metalcore
power metal
melodic death metal
gothic metal
hardcore
Search, Discovery & Recommendation
User Similarity
Paul (green) vs Elias (red)
http://anthony.liekens.net/
Discovery & Recommendation
Goal: Recreate Ishkur's Guide to EDM
Discovery & Recommendation
From Folksonomy to Taxonomy
Metal
Discovery & Recommendation
From Folksonomy to Taxonomy
Discovery & Recommendation
Creating an Artist Hierarchy
Attachment order: Artist Popularity
Attachment order: Artist Date
Discovery & Recommendation
Creating an Artist Hierarchy
Discovery & Recommendation
Creating a Personal Artist Hierarchy
Discovery & Recommendation
Transparency - Explaura
Discovery & Recommendation
Transparency - Explaura
2) Drag a tag to makeit bigger or smaller.
Discovery & Recommendation
Steerable recommendations - Explaura
1) Click to add a tag or artist
3) Receive recommendationsthat match the tag cloud
4) Get an explanation for
each recommendation
Discovery & Recommendation
Transparency - Pandora
http://flickr.com/photos/libraryman/1225285863/
Discovery & Recommendation
Transparency - Pandora
Technology loosing it's "cold"
Pandora feels like a smart friend to me. This friend can
articulate the reasons I love some of the things I love most
(songs) better than I can, but only because I have told it what I
like. This is one of my very favorite Prince songs and Pandora
knows just why I like it so much. And I didn't know how to say it
so well. Makes the technology seem very warm and reflective of my
feelings an identity. It's an extension of the user, not a cold,
isolating technology. I feel a part of Pandora some
times.
http://flickr.com/photos/libraryman/1225285863/
Discovery & Recommendation
Transparency Musical MadLibs
Norah Jones - Dont Know Why
Summary generated automatically using SML model:
This is soft rock, jazz song that is mellow and sad. It features piano, synthesizer, ambient sounds, and monotone, breathy vocals. It is a song with a slow tempo and with low energy that you might like to listen to while studying.
[Turnbull, Liu, Barrington & Lanckriet, ISMIR 2007]
Discovery & Recommendation
Cold start: new users
Need 'taste data' for new users
Results:
Awkward user enrollment
Poor recommendations
Solution
Represent taste 'portably'
Taste data can move with the user
Several efforts
Attention Profile Markup Language - APML
OpenTaste
Attention Profile Markup Language
APML
Turn this
Into this
Then this
Discovery & Recommendation
Last.fm Products using Tag Data
Search and Discovery
Last.fm Playground
http://playground.last.fm/multitag
* Filter results by tag (e.g. not rock)* up-and-coming* free download tracks* mention Klaas demo and that he'll also talk about some insights we gained from launching the demo on playground
Search and Discovery
Last.fm Playground
http://playground.last.fm/multitag
Multi Tag Search
Categories
Popular
Up-and-coming
Free downloads
Example queries
pop british 90s sad piano
catchy funny soundtrack
hilarious cover
"one hit wonder" 90s
american "guilty pleasure"
happy sad
relaxing speed metal
More information:Late breaking/demo sessionThursday 11.00 - 13.00Klaas Bosteels
* Filter results by tag (e.g. not rock)* up-and-coming* free download tracks* mention Klaas demo and that he'll also talk about some insights we gained from launching the demo on playground
Last.fm Playground: Islands of Music
Clustering listeners by tags
Last.fm Playground: Islands of Music
Clustering listeners by tags
13k randomly sampled Last.fm listeners
Each listener represented by a tag cloud
2000 dimensions (tags) 120 dimensions (SVD)
k-means clustering to extract 400 prototypical listeners
Self-organizing map (20x40)
http://playground.last.fm/iom
Search, Discovery & Recommendations
Vocabulary problem
Item similarity
Hierarchical clustering
Transparency
User cold start and APML
Last.fm
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Data and Tools
Pointers to all data at
SocialMusicResearch.org/data
Expert/Survey data
CAL-500
http://cosmal.ucsd.edu/cal/projects/AnnRet/AnnRet.php
1700 human generated musical annotations
500 popular western tracks
All Music
Available through commercial license
Genre, Styles and Moods for thousands of artists
Data and Tools
Game Data
ListenGame
26,000 annotations
250 songs
120 words
440 unique players
Available upon request from Doug Turnbull
MajorMiner
Available for browsing at:
http://majorminer.com/search
human tags
autotags
MIREX 2008
Data and Tools
Social tags
LastFM-ArtistTags2007
Tag data for over 20,000 artists
labs.strands.com
http://labs.strands.com/music/affinity/
Playlist co-occurrence data
Artist-Tag data for 4,000 artists
Data and Tools
TagWorks
Loads tag data from LastFM-ArtistTags2007
Simple-overlap similarity
tf-idf / cosine distance similarity
Agglomerative clustering of
tags, artists, users
Folksonomy Taxonomy algorithm
tags, artists, users
Last.fm crawler for user-tags
Written in the Java programming language
Uses the Minion search engine: https://minion.dev.java.net/
Available at: SocialMusicResearch.org/code
Data and Tools
Last.fm API 1.0
Tags applied by a user
Tags a user applied to specific artist/album/track
Tag clouds for artist/track or overall
Top artists/album/tracks for given tag
Creative Commons Attribution-Non Commercial-Share Alike License
http://www.audioscrobbler.net/data/webservices/
Data and Tools
Last.fm API 1.0 Examples
Social Tags for any MP3 (~ 70 lines of Python code)
Given MP3 file (without ID3)
Use Last.fm Fingerprinter to obtain ID3 info
Use ID3 info to obtain tags applied by Last.fm community
Tagger age vs vocabulary size (~ 85 lines of Python code)
Start with one or more seed users
Get friends, of friends, of friends ( large list of users)
For each user get tag applied, and demographic information (age)
Analyze data (tagger's vocabulary vs age)
http://SocialMusicResearch.org/code
Data and Tools
Last.fm API 2.0
user: add, remove or get tags for an artist/album/track
get similar tags for given tag
get top albums/artists/tracks for given tag
tag clouds for artist/track/overall
search for a tag
tags applied by a user
http://www.last.fm/api
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Future Research
Librarians, musicologists, anthropologists
What are tags? Who tags? Why do people tag? How do people use tags?
Is tagging behaviour and tag usage different for different demographics?
Can music tags be mapped to taxonomies?
Impact on
Digital libraries
Organizing and discovering music?
Musicology
Perceiving, describing, categorizing and talking about music?
Anthropology
Emergence of new subcultures?
Future Research
Can machines play tag?
Signal processing & statistical learning
Preliminary results look very promising but ...
What are the limitations and how far can we push them?
Quality in general?
What types of tags that cannot be learned?
Integration of machine learned tags in human interfaces?
Plenty of training data available!
And new fun challenges (data sparsity, noise, ...)
Future Research
User experience and UI design
Tagging
How do suggestions impact quality of tags?
Can suggestions be improved?
Social interaction through tags
Can how people communicate with tags be improved?
Discovery/browsing/steering recommendations
Beyond tag clouds?
Are tags a better way to organize playlists?
Future Research
More Open Questions
What can we learn about a user from her tagging behaviour?
Tag games
How do game tags differ from social tags?
How can you make games even more fun?
Computation of similarity
Combination of tags with other data sources?
Transparent recommendations
Using tags to explain recommendations?
Future Research
More Open Questions (2)
Semantic web
Connecting information and tags across the web?
E.g. Flickr & Last.fm
MOAT?
Internationalization?
Different communities tagging the same item with different languages? (Japanese vs Russian vs English)
World music?
Social tagging of other musical entities?
Detecting tag abuse?
Future Research
Tag Gardening
Tools for improving tags
Weeding
Seeding
Landscaping
Fertilizing
TagCare
MOAT
Future Research
Learn from Japanese Style Tagging?
http://joilab.ito.com/2008/02/nico-nico-douga.htmlhttp://www.nicovideo.jp/watch/sm9
User generated content
Mash-ups
Community
Tags as art form
Business model
Poster Session 2c: Mon 16.00 18.00
Ternary Semantic Analysis of Social Tags for Personalized Music Recommendation
P. Symeonidis, M. Ruxanda, A. Nanopoulos and Y. Manolopoulos
Five Approaches to Collecting Tags for Music
D. Turnbull, L. Barrington and G. Lanckriet
MoodSwings: A Collaborative Game for Music Mood Label Collection
Y. Kim, E. Schmidt and L. Emelle
ISMIR 2008
Poster Session 2c: Mon 16.00 18.00
Collective Annotation of Music From Multiple Semantic Categories
Z. Duan, L. Lu and C. Zhang
Connecting the Dots: Music Metadata Generation, Schemas and Applications (*)
N. Corthaut, S. Govaerts, K. Verbert and E. Duval
The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree? (*)
M. Sordo, O. Celma, M. Blech and E. Guaus
ISMIR 2008
Poster Session 2d: Mon 16.00 18.00
A Web of Musical Information
Y. Raimond and M. Sandler
Uncovering Affinity of Artists to Multiple Genres From Social Behaviour Data
C. Baccigalupo, J. Donaldson and E. Plaza
Oh Oh Oh Whoah! Towards Automatic Topic Detection in Song Lyrics
F. Kleedorfer, P. Knees and T. Pohle
ISMIR 2008
Poster Session 3a: Tue 11.00 13.00
Multi-Label Classification of Music Into Emotions
K. Trohidis, G. Tsoumakas, G. Kalliris and I. Vlahavas
Poster Session 5a: Wed 11.00 13.00
Multiple-Instance Learning for Music Information Retrieval
M. Mandel and D. Ellis
MIREX Poster Session: Wed 16.00 18.00
Audio Tag Classification
M. Mandel, T. Bertin-Mahieux, G. Tsoumakas, D. Turnbull & L. Barrington, G. Peeters
ISMIR 2008
Late-Breaking / Demo Session: Thu 11.00 13.00
Music Retrieval Based on Social Tags: A Case Study
K. Bosteels, E. Kerre, and E. Pampalk
Herd the Music - A Social Music Annotation Game
L. Barrington, D. OMalley, D. Turnbull, and G. Lanckriet
Music and Lyrics: Can Lyrics Improve Emotion Estimation for Music?
Daniel C. Wu Jr et al.
MOODY: A Web-Based Music Mood Classification and Recommendation System
Xiao Hu et al.
Creating Transparent, Steerable Recommendations
P. Lamere and F. Maillet
ISMIR 2008
From genres to tags:
Music information retrieval in the age of social tagging
Editors: J.-J. Aucouturier and E. Pampalk
Scanning the Dial: The Rapid Recognition of Music Genres
R. O. Gjerdingen and D. Perrott
Social Tagging and Music Information Retrieval
P. Lamere
Autotagger a Model for Predicting Social Tags from
Acoustic Features
T. Bertin-Mahieux, D. Eck, F. Maillet and P. Lamere
Learning Latent Semantic Models for Music
M. Levy and M. Sandler
A Web-Based Game for Collecting Music Metadata,
M. Mandel and D. Ellis
Journal of New Music Research
Special issue to appear in 2008
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Conclusions
SocialMusicResearch.org
Wiki
Slides
Data
Code
Bibliography
You can participate
Participate
The Fundamental Theorem
of Music Informatics
Music is created by humans for other humans, and humans can bring a tremendous amount of contextual knowledge to bear on anything they do; in fact, they can't avoid it, and they're rarely conscious of it. But computers can never bring much contextual knowledge to bear, often none at all, and never without being specifically programmed to do so. Therefore doing almost anything with music by computers is very difficult;many problems are essentially intractable. -- Don Byrd, January 2008
(I wrote the above statement, which I described with tongue firmly in cheek as a "theorem" -- a more accurate term would be "axiom" or "dogma" -- for my Spring 2008 graduate seminar, Organization and Searching of Musical Information. It's probably a bit too strongly worded, but I think it really comes close to describing a very basic problem that most music-informatics research has to deal with.) --Don Byrd, September 2008
Acknowledgments
Outline
What are social tags?
Why do people tag?
Issues with social tags
Other sources of tags
Search, Discovery & Recommendation
Data & Tools
Future Research
Conclusion
Discussion
Bibliography
http://SocialMusicResearch.org
C. Baccigalupo, E. Plaza, J. Donaldson. Uncovering affinity of artists to multiple genres from social behaviour data. ISMIR 2008.
L. Barrington, D. OMalley, D. Turnbull, and G. Lanckriet. Herd the Music - A Social Music Annotation Game. ISMIR 2008.
T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere. Autotagger: A model for predicting social tags from acoustic features on large music databases. In Journal of New Music Research, 2008 (to appear).
K. Bosteels, E. Kerre, and E. Pampalk. Music Retrieval Based on Social Tags: A Case Study. ISMIR 2008.
A. Craft, G. Wiggins, T. Crawford. How Many Beans Make Five? The Consensus Problem in Music-Genre Classification and a New Evaluation Method for Single-Genre Categorisation Systems. ISMIR 2007.
N. Corthaut, S. Govaerts, K. Verbert, and E. Duval. Connecting the Dots: Music Metadata Generation, Schemas and Applications. ISMIR 2008.
Z. Duan, L. Lu and C. Zhang. Collective Annotation Of Music From Multiple Semantic Categories. ISMIR 2008.
R. Gjerdingen and D. Perrott. Scanning the dial: The Rapid Recognition of Music Genre. In Journal of New Music Research, 2008 (to appear).
M. Guy & E. Tonkin. Folksonomies: Tidying up Tags? D-Lib Magazine, January 2006:12(1).
X. Hu, M. Bert, & J. S. Downie. Creating a simplified music mood classification ground-truth set. ISMIR 2007.
Xiao Hu et al. MOODY: A Web-Based Music Mood Classification and Recommendation System. ISMIR 2008.
Y. Kim, E. Schmidt and L. Emelle. MoodSwings: A Collaborative Game For Music Mood Label Collection. ISMIR 2008.
F. Kleedorfer, P. Knees and T. Pohle. Oh Oh Oh Whoah! Towards Automatic Topic Detection in Song Lyrics. ISMIR 2008.
P. Knees, M. Schedl, T. Pohle, and G. Widmer. An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. ACM MM 2006.
P. Lamere. Social Tagging and Music Information Retrieval. Journal of New Music Research 2008 (to appear).
P. Lamere and F. Maillet. Creating Transparent, Steerable Recommendations. ISMIR 2008.
E. L. M. Law, L. von Ahn, R. B. Dannenberg, and M. Crawford. TagATune: A game for music and sound annotation. ISMIR 2007.
M. Levy & M. Sandler. Learning Latent Semantic Models for Music from Social Tags. In Journal of New Music Research, 2008 (to appear).
M. I. Mandel and D. P. W. Ellis. A Web-Based Game for Collecting Music Metadata. In Journal of New Music Research, 2008 (to appear).
M. I. Mandel and D. P. W. Ellis. Multiple-instance learning for music information retrieval. ISMIR 2008.
Nielsen Report: Nielsen soundscan state of the industry. 2008 Convention of the National Association of Recording Merchandisers, 2008. http://www.narm.com/2008Conv/StateoftheIndustry.pdf.
E. Peterson. Beneath the metadata: Some philosophical problems with folksonomy. D-Lib Magazine 2006:12(11). http://www.dlib.org/dlib/november06/peterson/11peterson.html
E. Pampalk, A. Flexer, and G. Widmer. Hierarchical Organization and Description of Music Collections at the Artist Level. ECDL 2005.
E. Pampalk and M. Goto. MusicSun: A New Approach to Artist Recommendation. ISMIR 2007.
E. Pampalk and M. Goto. MusicRainbow: A New User Interface to Discover Artists Using Audio-based Similarity and Web-based Labeling. ISMIR 2006.
Y. Raimond and M. Sandler. A Web of Musical Information. ISMIR 2008.
R. Sinha. Tagging From Personal to Social. SXSW 2006. http://rashmisinha.com/2006/03/12/my-slides-for-tagging-20-panel-at-sxsw/
M. Sordo, O. Celma and M. Blech. The Quest For Musical Genres: Do the Experts and the Wisdom of Crowds Agree? ISMIR 2008.
P. Symeonidis, M. Ruxanda, A. Nanopoulos and Y. Manolopoulos. Ternary Semantic Analysis Of Social Tags For Personalized Music Recommendation. ISMIR 2008.
A. E. Thompson. Playing Tag: An Analysis of Vocabulary Patterns and Relationships Within a Popular Music Folksonomy. A Masters Paper for the M.S. in L.S. degree. April, 2008. http://etd.ils.unc.edu/dspace/bitstream/1901/535/1/abbeythompson.pdf
K. Trohidis, G. Tsoumakas, G. Kalliris and I. Vlahavas, Multi-Label Classification of Music Into Emotions. ISMIR 2008.
D. Turnbull, L. Barrington, G. Lanckriet. Five Approaches to Collecting Tags for Music. ISMIR 2008.
D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. Towards musical query-by-semantic description using the CAL500 data set. SIGIR 2007.
D. Turnbull, R. Liu, L. Barringon, and G. Lanckriet. A game-based approach for collecting semantic annotations of music. ISMIR 2007.
L. von Ahn and L. Dabbish. Labeling images with a computer game. SIGCHI 2004.
http://www.cs.cmu.edu/~biglou/ESP.pdf
D. Weinberger. How tagging changes peoples relationship to information and each other. Pew Internet & American Life Project 2007. http://www.pewinternet.org/pdfs/PIP_Tagging.pdf
K. Weller. Folksonomies and Ontologies. Two New Players in Indexing and Knowledge Representation. 2007. http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/35/1204288118weller009_.htm
Daniel C. Wu Jr et al. Music and Lyrics: Can Lyrics Improve Emotion Estimation for Music? ISMIR 2008.
Sun Microsystems, Inc.
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