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					Usability

Fujinaga 2003
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.

•   Design criteria for music recommender systems
•   Survey of research into musical taste
•   Review of music recommenders
     – Provide personalized content to users
          • Messages
          • List of stories
          • Artwork
     – Collaborative filtering (collect users’ opinions, ranking)
     – Content-based filtering
•   Limitations:
     – Inadequate raw data (editorial information)
     – Lack of quality control (user preference)
     – Lack of user preferences for new recordings
          • Content-based analysis needed for new recordings
     – Presentation (mostly simple lists)
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.

• Goals
     – Simple to use with minimum of input
     – More effort in providing input lead to better
       recommendations
     – Choice of music based on preferences, style, or mood

• Use existing research into factors affecting musical
  taste
     – Social psychology
     – Demographics for marketing
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.

•   Existing research
     –   Stable extraverts: solid predictable music
     –   Stable introverts: classical and baroque styles
     –   Unstable extraverts: romantic music expressing overt emotions
     –   Unstable introverts: mystical and impressionistic romantic works
     –   Aggressive: heavy metal or hard rock
     –   Japanese adolescents: classical or jazz
     –   Critical age: mean 23.5 years old
     –   Occupation
          • Dressmakers: moderately slow
          • Typist: fast tempo
     – Socio-economic background
          • Upper class women: classical
          • Working class men: hillbilly (Indiana)
     – Consistency in ranking of classical and popular music
     – Enjoyment correlates to labeling (“romantic”, “Nazi”, none) or known
       composer’s name
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.


• Factors affecting music preference
     –   Age
     –   Origin
     –   Occupation
     –   Socio-economic background
     –   Personality
     –   Gender
     –   Musical education
     –   Familiarity with the music or style
     –   Complexity of music
     –   Lyrics
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.


• Genres / styles
     – AllMusicGuide.com: 531
     – Amazon,com:        719
     – MP3.com            430

• Moods
     – 8 clusters with 67 moods (Hevner)
     – 10 clusters with 52 moods (Farnsworth 1958)
     – Features: tempo, tonality, distinctiveness of
       rhythm, pitch height
Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender
success. International Symposium on Music Information Retrieval. 204-8.

Techniques for music recommenders

• Collaborative filtering
     – Feedback from users: ratings, annotations, time spent
• Content-based filtering
     –   Problem of extracting musical semantics from raw signal
     –   Low-level features; notes, timbre, rhythm
     –   High-level features: adjectives
     –   Transcription, instrument identification, genre classifier
     –   Similarity measure from user supplied example (Welsh et al.)
          • 1248 features, 10-15 second samples, k-NN
Kim, J.-Y., and N. Belkin. 2002. Categories of music description and search terms
and phrases used by non-music experts. International Symposium on Music
Information Retrieval. 209-14.

• Information needs (music as information)
    –   Information-seeking towards the satisfaction of user
    –   Why does the user seek information?
    –   What purpose does the user believe it will serve?
    –   What use does it serve when found?
• Three basic “human needs”
    – Physiological (food, water, shelter)
    – Affective (emotional needs, e.g.: attainment, domination)
    – Cognitive (need to plan, need to learn skills)
• Music IR has concentrated on cognitive needs
    – Not enough user need studies
    – Ignored affective needs
    – Ignored musical information needs
Kim, J.-Y., and N. Belkin. 2002. Categories of music description and search terms
and phrases used by non-music experts. International Symposium on Music
Information Retrieval. 209-14.

• Purpose: To relate descriptions of affect to specific
  musical works
    – “means” for listeners to express their information “needs”
• Seven classical music: 22 subjects
    – 11 s.: Words to describe the music
    – 11 s.: Words used to search for the music
• Words used grouped into seven categories
    – Mostly emotions and occasions or filmed events
• Subjects had no formal musical training
    – Used non-formal music terms
    – Terms not found in music query systems
Futrelle, J., and J. Stephen Downie. 2002. Interdisciplinary communities and
research issues in music information retrieval. International Symposium on Music
Information Retrieval. 215-21.


• Two main problems in MIR research
    – No evaluation method
    – Lack of user-need studies
• Overemphasis on research in QBH systems is
  unsupportable given their doubtful usefulness
• Research into recommender systems common in
  other domain is inexplicably rare
• Lack of user interface research
• Undue emphasis on Western music
Futrelle, J., and J. Stephen Downie. 2002. Interdisciplinary communities and
research issues in music information retrieval. International Symposium on Music
Information Retrieval. 215-21.


First Principles of MIR:
• MIR systems are developed to serve the needs of
   particular user communities.
• MIR techniques are evaluated according to how well
   they meet the needs of user communities.
• MIR techniques are evaluated according to agreed-
   upon measures against agreed-upon collections of
   data, so that meaningful comparisons can be made
   between different research efforts.
Blandford, A., and H. Stelmaszewska. 2002. Usability of musical digital libraries:
A multimodal analysis. International Symposium on Music Information Retrieval.
231-7.
Evaluation of four web-accessible music libraries.
•   www.nzdl.org music
•   www.nzdl.org video
•   ABC Tunefinder
•   Folk Music Collection

•   Aimed at different user community (different levels of
    technological and musical knowledge)
•   Too many file format choice for novices
             Other usability studies
• Variations (Indiana Music Library)
• Design guidelines and user-centered digital libraries
  (Theng et al.)

				
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posted:10/18/2012
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