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Semantic Description of Musical Audio

IPEM – DEPARTMENT OF MUSICOLOGY | GHENT UNIVERSITY – BELGIUM. Semantic Description of Musical Audio. EVELINE HEYLEN | Eveline.Heylen@UGent.be PROMOTORS | PROF. DR. LEMAN (IPEM) & PROF. DR. IR. MARTENS (ELIS). PROBLEM SPECIFICATION. Background?. PROBLEM SPECIFICATION. Problem?

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Semantic Description of Musical Audio

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  1. IPEM – DEPARTMENT OF MUSICOLOGY | GHENT UNIVERSITY – BELGIUM Semantic Description of Musical Audio EVELINE HEYLEN | Eveline.Heylen@UGent.be PROMOTORS | PROF. DR. LEMAN (IPEM) & PROF. DR. IR. MARTENS (ELIS)

  2. PROBLEM SPECIFICATION Background?

  3. PROBLEM SPECIFICATION Problem? COMPLEX relation between: • structural description of musical signals • semantic description of musical experiences

  4. PROBLEM SPECIFICATION My research task… • structural annotation: i.e. rythm, melody, tempo, harmony… • semantic annotation: i.e. study concerning appreciation of perceived musical characteristics (affects, emotions…) • necessary for: • training of computer algorithms • understanding user behaviour

  5. PROBLEM SPECIFICATION Strategic applications? • audio-mining • new and flexible MIR • interactive multimedia • devices dealing with gestures, affect… • artistic and every day use • brain research • determining brain activity

  6. EXAMPLE OF SEMANTIC RESEARCH IN MUSIC

  7. SPEAC What? Sensitive Processing of Artistic Content research on: • effects of gender, taste, education, musical background… • structures in the emotional space • relations emotional responses ~ auditory features

  8. SPEAC How? • 100 students • electronic evaluation • 60 excerpts • 30” duration • different genres • 15 bipolar adjectives • 7-point scale  correlations + factor analysis

  9. SPEAC VALENCE  base = (un)favorable qualifications ACTIVITY  base = movement or power INTEREST  base = interest

  10. SPEAC Further… • familiarity • woman • broad tasted subjects • musical training Examples most moving | most exciting

  11. MASTER’S THESIS RESULTS

  12. TONALITY Motive? • publication Toiviainen & Krumhansl • concurrent probe tone method • applicability? feasibility? • results  new method for inducing tonality

  13. TONALITY Concrete? ≠ artificial stimuli, but natural stimuli • world • daily touch with natural sound ≠ indication task, butproduction task • ecological value • individual capacities

  14. TONALITY Experiment • 26 subjects • 20 one-minute excerpts • classical and non-classical music • consistent timbre (strings) and equal tempo (120 MM) • sing the most fitting tones… • learning and performing by imitation  undertone method

  15. TONALITY NOTE: Y-axis = sung total in time X-axis = circle of fifths C auditive analysis tone centre c G - D - A score analysis D minor

  16. TONALITY NOTE: Y-axis = sung total in time X-axis = circle of fifths C – G – D – A auditive analysis pentatonic scale on D = heterogeneous output…

  17. TONALITY Additional appreciation experiment • same 26 subjects • same 20 one-minute excerpts • draw musical progress + fill in questionnaire  graphical ratings // musical aspects?  results questionnaire // SPEAC?

  18. compilation kanasztancok: angular, pointed patterns – standstill!

  19. compilation horror: main pattern = scribble

  20. compilation tiersen: small waving patterns, steady periods

  21. FUTURE PLANNING

  22. FUTURE PLANNING • finish tonality research • improvements • compare with key extraction model (IPEM toolbox) • music annotation • large experiment (750 subjects) • structural features (tonality, rythm, melody…) • semantic features (musical affect, emotion…) • algorithm training + understanding user behavior • sensory-motor theory of perception

  23. Thank you for your attention! Questions?

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