Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models
This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"006:55"
]
|
| contactPoint |
{
"fn": "Adam Wunderlich",
"hasEmail": "mailto:adam.wunderlich@nist.gov"
}
|
| description | This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models. |
| distribution |
[
{
"title": "GitHub repository",
"format": "python source code",
"accessURL": "https://github.com/usnistgov/NoiseGAN",
"description": "GitHub repository"
}
]
|
| identifier | ark:/88434/mds2-2695 |
| issued | 2022-07-08 |
| keyword |
[
"band-limited noise",
"colored noise",
"fractional Brownian motion",
"fractional Gaussian noise",
"impulsive noise",
"machine learning",
"power law noise",
"shot noise",
"time series"
]
|
| landingPage | https://data.nist.gov/od/id/mds2-2695 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2022-07-03 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| theme |
[
"Mathematics and Statistics:Image and signal processing",
"Mathematics and Statistics:Modeling and simulation research"
]
|
| title | Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models |
