Understanding Gaussian Noise Mismatch: A Hellinger Distance Approach

This paper explores noise-mismatched models using the Hellinger distance. In many applications, the design/training stage often assumes an independent and identically distributed (i.i.d.) Gaussian prior noise, but the real world introduces Gaussian noise with arbitrary covariance, creating a mismatc...

Full description

Saved in:
Bibliographic Details
Main Authors: Huang, Kexin, Shi, Chaohua, Gan, Lu, Liu, Hongqing
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
f-divergence
Gaussian noise
Hellinger distance
Noise mismatch
Reliability theory
Robustness
Signal processing
Speech processing
Uncertainty
Unpaired Image-to-Image translation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper explores noise-mismatched models using the Hellinger distance. In many applications, the design/training stage often assumes an independent and identically distributed (i.i.d.) Gaussian prior noise, but the real world introduces Gaussian noise with arbitrary covariance, creating a mismatch. We analyze the impact on system output and study optimal injected noise intensity for training/design. While theory assumes Gaussian sources, it provides guidance for non-Gaussian settings too. Experiments with Cycle-GAN for image-to-image translation validate the theory, producing results consistenting with derivations. Overall, this work provides theoretical and empirical insights into designing systems robust to noise uncertainties beyond simplified assumptions.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10446269