Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2024-02-22
Metadata Last Updated:
2023-06-07 00:00:00
Date Created:
N/A
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Data Provided by:
generative adversarial network
Dataset Owner:
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| Title | Noise Datasets for Evaluating Deep Generative Models |
|---|---|
| Description | 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. |
| Modified | 2023-06-07 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | generative adversarial network , machine learning , time series , band-limited noise , power law noise , shot noise , impulsive noise , colored noise , fractional Gaussian noise , fractional Brownian motion |
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"language": [
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"title": "Noise Datasets for Evaluating Deep Generative Models",
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