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Learning to fuse music genres with generative adversarial dual learning

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Paper presentation at ICDM 2017, see more information at http://people.cs.vt.edu/czq/publication/fusiongan/

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Learning to fuse music genres with generative adversarial dual learning

  1. 1. Learning to Fuse Music Genres with Generative Adversarial Dual Learning Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, Chang-Tien Lu Department of Computer Science, Virginia Tech Center for Music Technology, Georgia Institute of Technology
  2. 2. PaperPresentation Agenda Motivation How to be creative? Examples Method GAN extension Domain fusion Evaluation Quantitative study Listening test Demo Q & A
  3. 3. Motivation How to be creative? Art workers Drawing a picture by imitation w/ fusion Example Vincent Van Gogh imitating Japanese art (ukiyo-e) early paintings ukiyo-e later paintings Fuse ideas Combining things that don't normally go together.
  4. 4. Motivation How to be creative? Fuse ideas Combining things that don't normally go together. Researcher Writing a paper by imitation w/ fusion Ph.D. student Get an idea after reading a lot of papers
  5. 5. Motivation Music generation problem Problem Create new music after listening to music of different genres. Challenge How to combine the ideas of listened music in an unsupervised fashion? Related work Domain transferring, rather than combining Solution Generative adversarial networks (GAN) extension
  6. 6. Method Fusion GAN Three domains: Domain A/B: given music Domain F: generated music Components of GAN G/D:Generator / Discriminator X_a/X_b: Data Problem settings Learn a balanced distribution from X_a / X_b Task
  7. 7. Global loss function Method: Fusion GAN = From the perspectives of each D
  8. 8. Method: Fusion GAN • Balance constraint: perfect mix is half-half • Domain A • Domain F:keep same distance from A and B • FusionGAN Optimality Theorem • convergence guarantee
  9. 9. Evaluation Quantitative Study Music Genres Intrinsic property Define intrinsic properties of music genre Normalized Pitch Distribution (NPD) 88 keys into C/C#/D/D#/E/F/F#/G/G#/ A/A#/B Duration Distribution (DD) Each note has a duration
  10. 10. Evaluation Quantitative Study A B F Normalized Pitch Distribution Duration Distribution (DD)
  11. 11. Evaluation Listening test via user survey How much it is thought as fusion music Fusion Level Given fused music, choose better fusion (A)pure jazz (B)pure folk (C)mixture of jazz and folk (D)neither Rate the level of its composer (A)expert (B)newbie (C)robot
  12. 12. Thank you! Q&A https://github.com/aquastar/fusion_gan Code has been released! Demo

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