DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network
Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model t...
Main Authors: | Ping-Huan Kuo, Ssu-Ting Lin, Jun Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-05-01
|
Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/1550147720923529 |
Similar Items
-
Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders
by: Rim Shayakhmetov, et al.
Published: (2020-04-01) -
Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
by: Xiaoqing Wang, et al.
Published: (2018-12-01) -
SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation
by: Debapriya Hazra, et al.
Published: (2020-12-01) -
Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders
by: Rim Shayakhmetov, et al.
Published: (2020-08-01) -
Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem
by: Ensen Wu, et al.
Published: (2020-01-01)