255 citations · 780 across the 58 of their papers we have counts for
65 papers
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…
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
Sarthak Yadav, Sergios Theodoridis, Lars Kai Hansen +1
In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-gl…
Speech inpainting: Context-based speech synthesis guided by video
Juan F. Montesinos, Daniel Michelsanti, Gloria Haro +2
Audio and visual modalities are inherently connected in speech signals: lip movements and facial expressions are correlated with speech sounds. This motivates studies that incorpor…
PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs. Such bounds are widesp…
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (st…
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise
Christian Heider Nielsen, Zheng-Hua Tan
In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time…
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting
Iván López-Espejo, Ram C. M. C. Shekar, Zheng-Hua Tan +2
In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstra…
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attri…
Minimum Processing Near-end Listening Enhancement
Andreas Jonas Fuglsig, Jesper Jensen, Zheng-Hua Tan +3
The intelligibility and quality of speech from a mobile phone or public announcement system are often affected by background noise in the listening environment. By pre-processing t…
Improving Label-Deficient Keyword Spotting Through Self-Supervised Pretraining
Holger Severin Bovbjerg, Zheng-Hua Tan
Keyword Spotting (KWS) models are becoming increasingly integrated into various systems, e.g. voice assistants. To achieve satisfactory performance, these models typically rely on…
Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay
Claus Meyer Larsen, Peter Koch, Zheng-Hua Tan
Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in…
Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface
Cristian J. Vaca-Rubio, Roberto Pereira, Xavier Mestre +4
Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interac…
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars
Cristian J. Vaca-Rubio, Dariush Salami, Petar Popovski +3
Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home,…
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) present…
Complex Recurrent Variational Autoencoder with Application to Speech Enhancement
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
As an extension of variational autoencoder (VAE), complex VAE uses complex Gaussian distributions to model latent variables and data. This work proposes a complex recurrent VAE fra…
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification
Achintya kr. Sarkar, Zheng-Hua Tan
Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extract…
Deep Spoken Keyword Spotting: An Overview
Iván López-Espejo, Zheng-Hua Tan, John Hansen +1
Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep le…
Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index
Andreas Jonas Fuglsig, Jan Østergaard, Jesper Jensen +3
This paper considers speech enhancement of signals picked up in one noisy environment which must be presented to a listener in another noisy environment. Recently, it has been show…
Radio Sensing with Large Intelligent Surface for 6G
Cristian J. Vaca-Rubio, Pablo Ramirez-Espinosa, Kimmo Kansanen +2
This paper leverages the potential of Large Intelligent Surface (LIS) for radio sensing in 6G wireless networks. Major research has been undergone about its communication capabilit…
Design of AoI-Aware 5G Uplink Scheduler UsingReinforcement Learning
Chien-Cheng Wu, Petar Popovski, Zheng-Hua Tan +1
Age of Information (AoI) reflects the time that is elapsed from the generation of a packet by a 5G user equipment(UE) to the reception of the packet by a controller. A design of an…
Remote Anomaly Detection in Industry 4.0 Using Resource-Constrained Devices
Anders E. Kalør, Daniel Michelsanti, Federico Chiariotti +2
A central use case for the Internet of Things (IoT) is the adoption of sensors to monitor physical processes, such as the environment and industrial manufacturing processes, where…
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able t…
Improvement of Noise-Robust Single-Channel Voice Activity Detection with Spatial Pre-processing
Max Væhrens, Andreas Jonas Fuglsig, Anders Post Jacobsen +4
Voice activity detection (VAD) remains a challenge in noisy environments. With access to multiple microphones, prior studies have attempted to improve the noise robustness of VAD b…
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing
Wei Rao, Yihui Fu, Yanxin Hu +11
The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing. The challenge consists of two separate…