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.

Data and Resources

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identifier ark:/88434/mds2-2695
issued 2022-07-08
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 National Institute of Standards and Technology
resource-type Dataset
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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • band-limited-noise
  • ckan
  • colored-noise
  • fractional-brownian-motion
  • fractional-gaussian-noise
  • geo
  • geoss
  • impulsive-noise
  • machine-learning
  • national
  • north-america
  • power-law-noise
  • shot-noise
  • time-series
  • united-states
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Adam Wunderlich
maintainer_email adam.wunderlich@nist.gov
metadata_created 2025-11-21T03:04:57.603666
metadata_modified 2025-11-21T03:04:57.603670
notes 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.
num_resources 1
num_tags 17
title Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models