Conference paper Open Access

MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer

Gino Brunner; Andres Konrad; Yuyi Wang; Roger Wattenhofer

Citation Style Language JSON Export

  "publisher": "ISMIR", 
  "DOI": "10.5281/zenodo.1492525", 
  "container_title": "Proceedings of the 19th International Society for Music Information Retrieval Conference", 
  "title": "MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer", 
  "issued": {
    "date-parts": [
  "abstract": "We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of handling polyphonic music with multiple instrument tracks, as well as modeling the dynamics of music by incorporating note durations and velocities. We show that MIDI-VAE can perform style transfer on symbolic music by automatically changing pitches, dynamics and instruments of a music piece from, e.g., a Classical to a Jazz style. We evaluate the efficacy of the style transfer by training separate style validation classifiers. Our model can also interpolate between short pieces of music, produce medleys and create mixtures of entire songs. The interpolations smoothly change pitches, dynamics and instrumentation to create a harmonic bridge between two music pieces. To the best of our knowledge, this work represents the first successful attempt at applying neural style transfer to complete musical compositions.", 
  "author": [
      "family": "Gino Brunner"
      "family": "Andres Konrad"
      "family": "Yuyi Wang"
      "family": "Roger Wattenhofer"
  "id": "1492525", 
  "event-place": "Paris, France", 
  "publisher_place": "Paris, France", 
  "type": "paper-conference", 
  "event": "International Society for Music Information Retrieval Conference (ISMIR 2018)", 
  "page": "747-754"
All versions This version
Views 131133
Downloads 8484
Data volume 35.2 MB35.2 MB
Unique views 127129
Unique downloads 7777


Cite as