Episode

Music Transformer and Machine Learning for Composition with Dr. Anna Huang

Finn interviews Composer and Machine Learning specialist Dr. Cheng-Zhi Anna Huang about the Music Transformer project at Google’s Magenta Labs. They discuss representations of music for machine learning, algorithmic music generation as a compositional aid, the JS Bach Google Doodle, how self-reference defines structure in music, and compare the musicality of different systems with example outputs.

Time Stamps

  • [0:01:05] Introducing Dr. Anna Huang
  • [0:03:43] JS Bach Google Doodle
  • [0:12:52] Representations of musical information for machine learning
  • [0:16:26] Music Transformer project
    • [0:25:15] RNN algorithm music sample
  • [0:25:45] ABA structure challenge for generative systems
    • [0:30:30] Vanilla Transformer algorithm music sample 
    • [0:32:07] Music Transformer algorithm music sample 
  • [0:36:30] Self Reference Visualisation (see blog post)
  • [0:43:27] Everyday music implications
  • [0:48:10] What this work says about music
    • [0:50:01] Music Transformer trained on Jazz Piano 

Show notes

Credits

The So Strangely Podcast is produced by Finn Upham, 2018. Algorithmic music samples from the blog post Music Transformer: Generating Music with Long-Term Structure, and included under the principles of fair dealing. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.