Social Tags and Music Information Retrieval (Part II)
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Social Tags and Music Information Retrieval (Part II)

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Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These ...

Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear

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Social Tags and Music Information Retrieval (Part II) Social Tags and Music Information Retrieval (Part II) Presentation Transcript

  • 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 Feature 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, a wesome, 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 Female Singing Unstructured/Unreliable representation Latent Semantic Model
    • Can address:
      • Synonym, Polysemy
      • Noise
    • Dimensionality reduction
  • Latent Semantic Analysis Query Latent Semantic Space Search
    • 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. Girl, GirlBand, Grrl, Girly Female Singer Female Female Vocalists Diva Chicks Woman Pop American Idol Pop RnB Rhythm & Blues Girl Pop LSA
    • 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 make it bigger or smaller. Discovery & Recommendation Steerable recommendations - Explaura 1) Click to add a tag or artist 3) Receive recommendations that 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 - Don’t 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> <Profile name=&quot;music&quot;> <ImplicitData> <Concepts> <Concept key=&quot; piano &quot; value =&quot;.81&quot;/> <Concept key=&quot; female vocalists &quot; value =&quot;.72&quot;/> <Concept key=&quot; indie pop &quot; value =&quot;.65&quot;/> <Concept key=&quot; art rock &quot; value =&quot;.60&quot;/> <Concept key=&quot; experimental &quot; value =&quot;.57&quot;/> <Concept key=&quot; great lyricist &quot; value =&quot;.55&quot;/> <Concept key=&quot; noise &quot; value =&quot;.35&quot;/> </Concepts> </ImplicitData> </Profile> </APML> Turn this Into this Then this APML
  • Discovery & Recommendation Last.fm Products using Tag Data
  • Search and Discovery Last.fm Playground http://playground.last.fm/multitag
  • 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
        • &quot;one hit wonder&quot; 90s
        • american &quot;guilty pleasure&quot;
        • happy sad
        • relaxing “speed metal”
    More information : Late breaking/demo session Thursday 11.00 - 13.00 Klaas Bosteels
  • 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://SocialM usicR esearch.org / code
  • Data and Tools Last.fm API 2.0
    • user: add , remove or get tags for an artist/a lbum/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.html http://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. O’Malley, 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 &quot;theorem&quot; -- a more accurate term would be &quot;axiom&quot; or &quot;dogma&quot; -- 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. O’Malley, 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.
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    • E. Pampalk and M. Goto. MusicSun: A New Approach to Artist Recommendation. ISMIR 2007.
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