Noise Datasets for Evaluating Deep Generative Models
Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions. Documentation of simulation methods and experiments with Generative Adversarial Networks (GANs) are given in the paper "Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks" and the associated software; see references below.
Complete Metadata
| bureauCode |
[ "006:55" ] |
|---|---|
| identifier | ark:/88434/mds2-3034 |
| issued | 2023-06-22 |
| landingPage | https://data.nist.gov/od/id/mds2-3034 |
| language |
[ "en" ] |
| programCode |
[ "006:045" ] |
| references |
[ "https://doi.org/10.1088/2632-2153/acee44", "https://github.com/usnistgov/NoiseGAN" ] |
| theme |
[ "Advanced Communications:Wireless (RF)", "Mathematics and Statistics:Image and signal processing", "Mathematics and Statistics:Modeling and simulation research" ] |
