19 citations · 40 across the 13 of their papers we have counts for
14 papers
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…
Exploring Resolution-Wise Shared Attention in Hybrid Mamba-U-Nets for Improved Cross-Corpus Speech Enhancement
Nikolai Lund Kühne, Jesper Jensen, Jan Østergaard +1
Recent advances in speech enhancement have shown that models combining Mamba and attention mechanisms yield superior cross-corpus generalization performance. At the same time, inte…
Learning Robust Spatial Representations from Binaural Audio through Feature Distillation
Holger Severin Bovbjerg, Jan Østergaard, Jesper Jensen +2
Recently, deep representation learning has shown strong performance in multiple audio tasks. However, its use for learning spatial representations from multichannel audio is undere…
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Nikolai Lund Kühne, Jesper Jensen, Jan Østergaard +1
With new sequence models like Mamba and xLSTM, several studies have shown that these models match or outperform the state-of-the-art in single-channel speech enhancement and audio…
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…
xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement
Nikolai Lund Kühne, Jan Østergaard, Jesper Jensen +1
While attention-based architectures, such as Conformers, excel in speech enhancement, they face challenges such as scalability with respect to input sequence length. In contrast, t…
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…
Noise-Robust Keyword Spotting through Self-supervised Pretraining
Jacob Mørk, Holger Severin Bovbjerg, Gergely Kiss +1
Voice assistants are now widely available, and to activate them a keyword spotting (KWS) algorithm is used. Modern KWS systems are mainly trained using supervised learning methods…
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…
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
Holger Severin Bovbjerg, Jesper Jensen, Jan Østergaard +1
In this paper, we propose the use of self-supervised pretraining on a large unlabelled data set to improve the performance of a personalized voice activity detection (VAD) model in…
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…