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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
| bureauCode |
[ "006:55" ] |
|---|---|
| identifier | ark:/88434/mds2-2695 |
| issued | 2022-07-08 |
| landingPage | https://data.nist.gov/od/id/mds2-2695 |
| language |
[ "en" ] |
| programCode |
[ "006:045" ] |
| theme |
[ "Mathematics and Statistics:Image and signal processing", "Mathematics and Statistics:Modeling and simulation research" ] |
