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MusicFX

MusicFX. Joe McCarthy Intel Research, Seattle. Active Environments. Most environments are passive -- deaf, dumb & blind -- unaware of their inhabitants and unable to assist them in a meaningful way. In contrast, an active environment is

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MusicFX

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  1. MusicFX Joe McCarthyIntel Research, Seattle

  2. Active Environments Most environments are passive --deaf, dumb & blind --unaware of their inhabitantsand unable to assist them in a meaningful way. In contrast, an active environment is a physical spacethat can sense and respondappropriatelyto the people and activities taking placewithin it.

  3. MusicFX

  4. Inspiration The Irritations behind MusicFX • UbiComp: technology in search of a problem? • Music in a Fitness Center • Popular in the Complaint Department • 25% of “feedback” focused on music • RSI: Repetitive Song Injury • 3 stations played, 91 available (DMX) • Squeaky Wheels • Vocal minority prevails over silent majority • Hangovers • This morning’s music = last night’s music

  5. The MusicFX System • Database of musical preferences • 91 genres, 5-point scale • Group Preference Arbitration algorithm • Group Preference Calculation • Candidate Identification • Weighted Random Selection operator • Environmental Events (algorithm triggers)

  6. An example GP Pr i Genre Person A B C D E i i 1 Alternative Rock 2 2 0 2 2 68 0.48 2 Hottest Hits 1 1 2 0 -2 38 0.27 3 New Music 1 1 1 0 0 35 0.25 4 Hot Country 2 0 0 0 -2 28 0.00 5 Dance 2 -1 1 -1 -1 28 0.00 6 World Beat 0 1 -1 1 -2 23 0.00 7 Traditional Country 1 0 0 -2 -2 17 0.00 8 50's Oldies 0 0 0 -1 -1 14 0.00 9 Heavy Metal -1 -1 -1 -1 -2 4 0.00 10 Polka -1 -1 -2 -2 -2 2 0.00

  7. The Success of MusicFX • Daily operation: Nov. 1997 – Dec. 2001 • Poll results (after 6 weeks): • +: increased variety, having some influence • -: abrupt changes, occasional “bad” music

  8. MusicFX: Fun Facts • Charts • Top 10, Bottom 13, Uncharted territory • Human Nature • Average individual musical preferences • Individual Preference Filter: NOT • Interesting anecdotes

  9. Top 10 Genres

  10. Bottom 13 Genres

  11. Other Unexperienced Genres

  12. Average Individual Preferences

  13. Individual Preference Filter Effect of IPF onCandidate Set Size

  14. MusicFX Anecdotes • Veto power & IPF • Uncommon variety • The Polka incident • The Chinese Music incident

  15. Optimizing (?) MusicFX • Problem: starvation • Outliers may never hear their music • Solution: market-based economy • Multi-agent system (Naghi Prasad) • Wealth is redistributed according to preferences • Policy issue • Please most people all the time vs.Please all people some of the time

  16. Market-Based MusicFX • Three kinds of people in this world • Happy (+2, +1), indifferent (0), unhappy (-1, -2) • Blessed are the Unhappy... • Happy people pay unhappy people • Eventually, unhappy people can afford their music • Is this good? • Only [simulated] time will tell • "I cannot give you the formula for success, but I can give you the formula for failure, which is: Try to please everybody.” -- Herbert B. Swope

  17. M -S E AX AT QUITABLE 100 80 60 of Satisfaction 40 % 20 Total Satisfaction Gini Coefficient 0 Max-Sat Equitable % change Max-Sat Equitable % change 0 50 100 7000 5776 -17.5% 0.26 0.01 94.2% % of members Market Simulation Results of Market-based MusicFX Simulation

  18. For more information • Joe McCarthy • mccarthy@intel-research.net • seattleweb.intel-reseach.net/people/mccarthy Thanks! … Questions?

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