Advanced search
1 file | 291.52 KB Add to list

A CycleGAN for style transfer between drum and bass subgenres

Len Vande Veire (UGent) , Tijl De Bie (UGent) and Joni Dambre (UGent)
Author
Organization
Abstract
In this work, we apply the CycleGAN image-to-image translation framework to Mel-scaled log-amplitude spectrograms, successfully realizing audio texture transfer between excerpts from two musically related genres. Such automatic musical transfer could provide music producers and DJs with new creative tools, e.g. to quickly prototype a remix of an existing song in another genre, or to use as an advanced effect during a live performance. We show that meaningful style transfer can be realized using only a limited amount of data and computational resources. A high-quality audio reconstruction is obtained from the generated amplitude spectrogram by simply using the phase of the original audio as an approximation for the phase of the generated spectrogram. This results in a significant quality improvement over traditional phase reconstruction methods.
Keywords
AI, CycleGAN, drum & bass, machine learning

Downloads

  • DS228.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 291.52 KB

Citation

Please use this url to cite or link to this publication:

MLA
Vande Veire, Len, et al. “A CycleGAN for Style Transfer between Drum and Bass Subgenres.” ML4MD at ICML2019, Proceedings, 2019.
APA
Vande Veire, L., De Bie, T., & Dambre, J. (2019). A CycleGAN for style transfer between drum and bass subgenres. In ML4MD at ICML2019, Proceedings. Long Beach, CA.
Chicago author-date
Vande Veire, Len, Tijl De Bie, and Joni Dambre. 2019. “A CycleGAN for Style Transfer between Drum and Bass Subgenres.” In ML4MD at ICML2019, Proceedings.
Chicago author-date (all authors)
Vande Veire, Len, Tijl De Bie, and Joni Dambre. 2019. “A CycleGAN for Style Transfer between Drum and Bass Subgenres.” In ML4MD at ICML2019, Proceedings.
Vancouver
1.
Vande Veire L, De Bie T, Dambre J. A CycleGAN for style transfer between drum and bass subgenres. In: ML4MD at ICML2019, Proceedings. 2019.
IEEE
[1]
L. Vande Veire, T. De Bie, and J. Dambre, “A CycleGAN for style transfer between drum and bass subgenres,” in ML4MD at ICML2019, Proceedings, Long Beach, CA, 2019.
@inproceedings{8619952,
  abstract     = {{In this work, we apply the CycleGAN image-to-image translation framework to Mel-scaled log-amplitude spectrograms, successfully realizing audio texture transfer between excerpts from two musically related genres. Such automatic musical transfer could provide music producers and DJs with new creative tools, e.g. to quickly prototype a remix of an existing song in another genre, or to use as an advanced effect during a live performance. We show that meaningful style transfer can be realized using only a limited amount of data and computational resources. A high-quality audio reconstruction is obtained from the generated amplitude spectrogram by simply using the phase of the original audio as an approximation for the phase of the generated spectrogram. This results in a significant quality improvement over traditional phase reconstruction methods.}},
  author       = {{Vande Veire, Len and De Bie, Tijl and Dambre, Joni}},
  booktitle    = {{ML4MD at ICML2019, Proceedings}},
  keywords     = {{AI,CycleGAN,drum & bass,machine learning}},
  language     = {{eng}},
  location     = {{Long Beach, CA}},
  pages        = {{3}},
  title        = {{A CycleGAN for style transfer between drum and bass subgenres}},
  url          = {{https://sites.google.com/view/ml4md2019/home}},
  year         = {{2019}},
}