ResearchTrend.AI
Employing Real Training Data for Deep Noise Suppression

Employing Real Training Data for Deep Noise Suppression

Ziyi Xu
Marvin Sach
Jan Pirklbauer
Tim Fingscheidt
ArXivPDFHTML

Papers citing "Employing Real Training Data for Deep Noise Suppression"

4 / 4 papers shown
Title
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Ziyi Xu
Maximilian Strake
Tim Fingscheidt
37
3
0
04 May 2022
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
Chandan K. A. Reddy
Vishak Gopal
Ross Cutler
Ebrahim Beyrami
R. Cheng
...
A. Aazami
Sebastian Braun
Puneet Rana
Sriram Srinivasan
J. Gehrke
76
312
0
16 May 2020
Supervised Speech Separation Based on Deep Learning: An Overview
Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang
Jitong Chen
SSL
57
1,359
0
24 Aug 2017
SEGAN: Speech Enhancement Generative Adversarial Network
SEGAN: Speech Enhancement Generative Adversarial Network
Santiago Pascual
Antonio Bonafonte
Joan Serrà
GAN
59
1,141
0
28 Mar 2017