U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Breadcrumb

  1. Home

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.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2023-06-07 00:00:00
Date Created: N/A
Views:
Data Provided by:
generative adversarial network
Dataset Owner: N/A

Access this data

Contact dataset owner Landing Page URL
Download URL
Table representation of structured data
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:adam.wunderlich@nist.gov
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
{
    "identifier": "ark:\/88434\/mds2-3034",
    "accessLevel": "public",
    "references": [
        "https:\/\/doi.org\/10.1088\/2632-2153\/acee44",
        "https:\/\/github.com\/usnistgov\/NoiseGAN"
    ],
    "contactPoint": {
        "hasEmail": "mailto:adam.wunderlich@nist.gov",
        "fn": "Adam Wunderlich"
    },
    "programCode": [
        "006:045"
    ],
    "@type": "dcat:Dataset",
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3034",
    "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.",
    "language": [
        "en"
    ],
    "title": "Noise Datasets for Evaluating Deep Generative Models",
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/ark:\/88434\/mds2-3034\/README.txt",
            "mediaType": "text\/plain"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/ark:\/88434\/mds2-3034\/README.txt.sha256",
            "mediaType": "text\/plain"
        }
    ],
    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
        "006:55"
    ],
    "modified": "2023-06-07 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "accrualPeriodicity": "irregular",
    "theme": [
        "Advanced Communications:Wireless (RF)",
        "Mathematics and Statistics:Image and signal processing",
        "Mathematics and Statistics:Modeling and simulation research"
    ],
    "issued": "2023-06-22",
    "keyword": [
        "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"
    ]
}

Was this page helpful?