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Is Disentanglement enough? On Latent Representations for Controllable
  Music Generation

Is Disentanglement enough? On Latent Representations for Controllable Music Generation

1 August 2021
Ashis Pati
Alexander Lerch
    CoGe
    DRL
ArXivPDFHTML

Papers citing "Is Disentanglement enough? On Latent Representations for Controllable Music Generation"

6 / 6 papers shown
Title
The Ethical Implications of Generative Audio Models: A Systematic
  Literature Review
The Ethical Implications of Generative Audio Models: A Systematic Literature Review
J. Barnett
26
25
0
07 Jul 2023
Domain Adversarial Training on Conditional Variational Auto-Encoder for
  Controllable Music Generation
Domain Adversarial Training on Conditional Variational Auto-Encoder for Controllable Music Generation
Jingwei Zhao
Gus Xia
Ye Wang
DRL
39
6
0
15 Sep 2022
Controllable Data Generation by Deep Learning: A Review
Controllable Data Generation by Deep Learning: A Review
Shiyu Wang
Yuanqi Du
Xiaojie Guo
Bo Pan
Zhaohui Qin
Liang Zhao
31
28
0
19 Jul 2022
MusIAC: An extensible generative framework for Music Infilling
  Applications with multi-level Control
MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control
Rui Guo
Ivor Simpson
C. Kiefer
Thor Magnusson
Dorien Herremans
16
24
0
11 Feb 2022
Latte: Cross-framework Python Package for Evaluation of Latent-Based
  Generative Models
Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
Alon Jacovi
Junyoung Lee
Alexander Lerch
DRL
18
1
0
20 Dec 2021
Evaluation of Latent Space Disentanglement in the Presence of
  Interdependent Attributes
Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes
Karn N. Watcharasupat
Alexander Lerch
23
2
0
11 Oct 2021
1