1 / 18

Computer Music Composition using Crowdsourcing and Genetic Algorithms

Jessica Keup. Computer Music Composition using Crowdsourcing and Genetic Algorithms. Problem Statement and Goal. Genetic algorithms (GAs) to create music With programmatic fitness, ineffective music With human input, fitness bottleneck Way to solve fitness bottleneck?

ray-gilliam
Download Presentation

Computer Music Composition using Crowdsourcing and Genetic Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Jessica Keup Computer Music Composition using Crowdsourcing and Genetic Algorithms

  2. Problem Statement and Goal • Genetic algorithms (GAs) to create music • With programmatic fitness, ineffective music • With human input, fitness bottleneck • Way to solve fitness bottleneck? • Creativity/collaboration for musical novices • Potential solution for GA fitness bottlenecks • Potential use for crowdsourcing Relevance and Significance

  3. Research Question Q: “When music that is created by a GA trained by a crowdsourced group is compared to music created by a GA trained by a small group, is the crowdsourced music more effective?” A: By running two instances of the same musical GA with those two training conditions, then having composers and musical laypeople review the results, the song effectiveness was about the same overall.

  4. Computer Music • Composition, performance, analysis, sound processing, sound production • Search problem with no optimal solution • GA suitability • First, with programmatic fitness only • Next, with human evaluation as fitness • Recurring bottleneck problem

  5. Fitness Bottleneck and Workarounds • GenJam– Biles (1994) • Audioserve- Yee-King (2000) • SBEAT3- Unemi (2002) • Constructive Adaptive User Interface (CAUI) - Legaspi et al. (2007) • Gartland-Jones and Copley (2003) • Unehara and Onisawa (2003) • Composition, Feedback, and Evolution Framework – Fu et al. (2009) identified the problem attempted a solution

  6. Crowdsourcing • Outsourcing to collective online intelligence • Pros • around-the-clock • inexpensive • fast • wisdom of crowd • Marketplaces such as • Cons • untrustworthiness • lack of skill • ethics of outsourcing

  7. Darwin Tunes • Crowdsourced compositional GA – MacCallum and Leroi • Evolectronica: Survival of the Funkiest • 641 generations of evolution • Not mTurk, not a formalized study Music Information Retrieval Evaluation eXchange (MIREX) • Urbano, Morato, Marrero, & Martin (2010) used mTurk • Crowdsourced ratings of music similarity • expert-level results on 2,810 rankings for $70.25

  8. GA choice - Melodycomposition • Considered code from VARIATIONS, master’s thesis, Spieldose, and CAUI • Melodycomposition – Craane on code.google.com • Uses Java Genetic Algorithms Package (JGAP) • Modifications: • 2 melodies (SA) • Additional fitness • Interaction with mTurk • Removal of GUI • Database persistence • # generations (11 & 200) [F#:7:QUARTER] [A#:4:QUARTER] [F#:6:EIGTH]

  9. Genre and Programmatic Fitness • Chorale-like genre • Instrumental • 2-part (soprano/bass) • List of fitness guidelines in addition to human ratings • After Large Skip • Consecutive Skips • Global Pitch Distribution • Interval • Parallel Motion • Proportion Notes/Rests • Range • Repeating Notes • Scale • Strong Beats

  10. Prototype and Task Setup • Modification of melodycomposition • Interaction with mTurk Java API • Webpage for participants, with php and JavaScript to appear on mTurk • MySQL database and Ubuntu server • IRB approval from Nova • IRB approval from ETSU

  11. Training GAs

  12. Evaluation by Reviewers and Composers

  13. Music • Small control group songs: 12345 • Large test group songs: 678910

  14. Difference between Reviewers’/Composers’ Test minus Control Effectiveness

  15. Combined Reviewer Ratings of All Music

  16. Combined Composer Ratings of All Music

  17. Reviewers’/Composers’ Artistic Effectiveness Ratings

  18. ImplicationsRecommendations • Test music slightly better overall, but not statically significant • Null hypothesis notrejected • Fine-tune rules in programmatic fitness function • Change rules weights • Avoid premature convergence (mutation rate?) • Compare to 200 generations of programmatic fitness only • Use Turkit • Use preference judgments instead of best/middle/worst • Use voting or limit HITs to one-per-worker

More Related