19 citations · 39 across the 14 of their papers we have counts for
9 papers · 1 filter
Listen first: Output-based multi-microphone speech enhancement
Panos Apostolidis, Svend Feldt, Zheng-Hua Tan +2
Traditionally, hearing-aid speech enhancement (SE) algorithms rely on input-based feature estimation, often derived by a voice activity detector (VAD), to configure beamformers. Ye…
Ranking the Impact of Contextual Specialization in Neural Speech Enhancement
Peter Leer, Svend Feldt, Zheng-Hua Tan +2
We systematically investigate neural speech enhancement systems, ranging from very small (10\,k parameters) to medium-large (2-5\,M parameters), which specialize to aco…
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining
Holger Severin Bovbjerg, Jan Østergaard, Jesper Jensen +1
Target-Speaker Voice Activity Detection (TS-VAD) is the task of detecting the presence of speech from a known target-speaker in an audio frame. Recently, deep neural network-based…
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of trainin…
How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses
Peter Leer, Jesper Jensen, Zheng-Hua Tan +2
Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed de…
Hearing-Loss Compensation Using Deep Neural Networks: A Framework and Results From a Listening Test
Peter Leer, Jesper Jensen, Laurel H. Carney +3
This article investigates the use of deep neural networks (DNNs) for hearing-loss compensation. Hearing loss is a prevalent issue affecting millions of people worldwide, and conven…
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performanc…
Investigating the Design Space of Diffusion Models for Speech Enhancement
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diff…
Joint Minimum Processing Beamforming and Near-end Listening Enhancement
Andreas J. Fuglsig, Jesper Jensen, Zheng-Hua Tan +3
We consider speech enhancement for signals picked up in one noisy environment that must be rendered to a listener in another noisy environment. For both far-end noise reduction and…