Data-driven modeling of noise time series with convolutional generative adversarial networks

Data-driven modeling of noise time series with convolutional generative adversarial networks

Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven modeling, it is important to determine to what exte...

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Bibliographic Details
Main Authors: Adam Wunderlich, Jack Sklar
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
time series
generative adversarial network (GAN)
power law noise
shot noise
impulsive noise
fractional gaussian noise
Online Access:https://doi.org/10.1088/2632-2153/acee44

Internet

https://doi.org/10.1088/2632-2153/acee44

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