Usability

1
Usability

• Fujinaga 2003

2
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)

3
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

4
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 composers name

5
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

6
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
  

7
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

8
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

9
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

10
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

11
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.

12
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

13
Other usability studies

• Variations (Indiana Music Library)
• Design guidelines and user-centered digital
  libraries (Theng et al.)