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Speech Enhancement using Sequence Modelling Neural Architectures and Spoken Large Language Models
Kühne, Nikolai Lund
(PI)
Tan, Zheng-Hua
(Supervisor)
Østergaard, Jan
(Supervisor)
Jensen, Jesper
(Supervisor)
Department of Electronic Systems
The Technical Faculty of IT and Design
Artificial Intelligence and Sound
Machine Learning
Overview
Fingerprint
Publications
(1)
Project Details
Status
Active
Effective start/end date
02/09/2024
→
31/08/2028
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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Squeeze-and-excitation Network (SENet)
Keyphrases
100%
Input Sequence
Keyphrases
33%
Sequence Length
Computer Science
33%
Research output
Research output per year
2025
2025
2025
1
Preprint
Research output per year
Research output per year
xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement
Kühne, N. L.
,
Østergaard, J.
,
Jensen, J.
&
Tan, Z.-H.
,
10 Jan 2025
,
6 p.
Research output
:
Working paper/Preprint
›
Preprint
File
Squeeze-and-excitation Network (SENet)
100%
Input Sequence
33%
Sequence Length
33%