b'Zheng-Hua Tan'

research

∙ 09/20/2023

Joint Minimum Processing Beamforming and Near-end Listening Enhancement

We consider speech enhancement for signals picked up in one noisy enviro...

0 Andreas J. Fuglsig, et al. ∙

research

∙ 06/01/2023

Masked Autoencoders with Multi-Window Attention Are Better Audio Learners

Several recent works have adapted Masked Autoencoders (MAEs) for learnin...

0 Sarthak Yadav, et al. ∙

research

∙ 06/01/2023

Speech inpainting: Context-based speech synthesis guided by video

Audio and visual modalities are inherently connected in speech signals: ...

0 Juan F. Montesinos, et al. ∙

research

∙ 03/29/2023

PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss

In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian ...

0 Deividas Eringis, et al. ∙

research

∙ 12/30/2022

PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models

In this paper we derive a PAC-Bayesian-Like error bound for a class of s...

0 Deividas Eringis, et al. ∙

research

∙ 11/19/2022

Filterbank Learning for Small-Footprint Keyword Spotting Robust to Noise

In the context of keyword spotting (KWS), the replacement of handcrafted...

0 Iván López-Espejo, et al. ∙

research

∙ 11/15/2022

Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder

By utilizing the fact that speaker identity and content vary on differen...

0 Yuying Xie, et al. ∙

research

∙ 11/03/2022

Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise

In recent years, significant progress has been made in deep model-based ...

0 Christian Heider Nielsen, et al. ∙

research

∙ 10/31/2022

Minimum Processing Near-end Listening Enhancement

The intelligibility and quality of speech from a mobile phone or public ...

0 Andreas Jonas Fuglsig, et al. ∙

research

∙ 10/04/2022

Improving Label-Deficient Keyword Spotting Using Self-Supervised Pretraining

In recent years, the development of accurate deep keyword spotting (KWS)...

0 Holger Severin Bovbjerg, et al. ∙

research

∙ 06/21/2022

Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface

Environmental scene reconstruction is of great interest for autonomous r...

0 Cristian J. Vaca-Rubio, et al. ∙

research

∙ 05/17/2022

User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars

Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sens...

0 Cristian J. Vaca-Rubio, et al. ∙

research

∙ 02/08/2022

Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Ch...

0 Fan Yu, et al. ∙

research

∙ 01/17/2022

On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification

Deep representation learning has gained significant momentum in advancin...

0 Achintya kr. Sarkar, et al. ∙

research

∙ 11/20/2021

Deep Spoken Keyword Spotting: An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords ...

6 Iván López-Espejo, et al. ∙

research

∙ 11/15/2021

Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index

This paper considers speech enhancement of signals picked up in one nois...

0 Andreas Jonas Fuglsig, et al. ∙

research

∙ 10/19/2021

Design of AoI-Aware 5G Uplink Scheduler UsingReinforcement Learning

Age of Information (AoI) reflects the time that is elapsed from the gene...

0 Chien-Cheng Wu, et al. ∙

research

∙ 10/12/2021

Remote Anomaly Detection in Industry 4.0 Using Resource-Constrained Devices

A central use case for the Internet of Things (IoT) is the adoption of s...

0 Anders E. Kalør, et al. ∙

research

∙ 09/06/2021

Optimal Prediction of Unmeasured Output from Measurable Outputs In LTI Systems

In this short article, we showcase the derivation of an optimal predicto...

0 Deividas Eringis, et al. ∙

research

∙ 04/12/2021

Improvement of Noise-Robust Single-Channel Voice Activity Detection with Spatial Pre-processing

Voice activity detection (VAD) remains a challenge in noisy environments...

0 Max Væhrens, et al. ∙

research

∙ 04/02/2021

INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing

The ConferencingSpeech 2021 challenge is proposed to stimulate research ...

0 Wei Rao, et al. ∙

research

∙ 03/27/2021

On TasNet for Low-Latency Single-Speaker Speech Enhancement

In recent years, speech processing algorithms have seen tremendous progr...

0 Morten Kolbæk, et al. ∙

research

∙ 03/23/2021

PAC-Bayesian theory for stochastic LTI systems

In this paper we derive a PAC-Bayesian error bound for autonomous stocha...

0 Deividas Eringis, et al. ∙

research

∙ 02/03/2021

Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification

In this paper, we propose a novel method that trains pass-phrase specifi...

0 Achintya Kumar Sarkar, et al. ∙

research

∙ 11/25/2020

Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding

In this letter, we propose a vocal tract length (VTL) perturbation metho...

0 Achintya kr. Sarkar, et al. ∙

research

∙ 11/16/2020

Assessing Wireless Sensing Potential with Large Intelligent Surfaces

Sensing capability is one of the most highlighted new feature of future ...

0 Cristian J. Vaca-Rubio, et al. ∙

research

∙ 10/12/2020

CC-Loss: Channel Correlation Loss For Image Classification

The loss function is a key component in deep learning models. A commonly...

0 Zeyu Song, et al. ∙

research

∙ 10/11/2020

Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization

Due to lack of data, overfitting ubiquitously exists in real-world appli...

0 Jiyang Xie, et al. ∙

research

∙ 10/09/2020

Audio-Visual Speech Inpainting with Deep Learning

In this paper, we present a deep-learning-based framework for audio-visu...

0 Giovanni Morrone, et al. ∙

research

∙ 08/21/2020

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose pu...

0 Daniel Michelsanti, et al. ∙

research

∙ 07/26/2020

UIAI System for Short-Duration Speaker Verification Challenge 2020

In this work, we present the system description of the UIAI entry for th...

0 Md Sahidullah, et al. ∙

research

∙ 07/12/2020

Data augmentation enhanced speaker enrollment for text-dependent speaker verification

Data augmentation is commonly used for generating additional data from t...

0 Achintya Kumar Sarkar, et al. ∙

research

∙ 06/11/2020

A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting

One of the beyond-5G developments that is often highlighted is the integ...

2 Cristian J. Vaca-Rubio, et al. ∙

research

∙ 05/30/2020

Exploring Filterbank Learning for Keyword Spotting

Despite their great performance over the years, handcrafted speech featu...

0 Iván López-Espejo, et al. ∙

research

∙ 05/15/2020

On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors

Applying x-vectors for speaker verification has recently attracted great...

0 Achintya Kumar Sarkar, et al. ∙

research

∙ 04/20/2020

OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer

A deep neural network of multiple nonlinear layers forms a large functio...

0 Xiaoxu Li, et al. ∙

research

∙ 04/06/2020

Vocoder-Based Speech Synthesis from Silent Videos

Both acoustic and visual information influence human perception of speec...

0 Daniel Michelsanti, et al. ∙

research

∙ 02/04/2020

Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective

This paper studies context-aware recommendations in the television domai...

0 Miklas S. Kristoffersen, et al. ∙

research

∙ 10/22/2019

Adversarial Example Detection by Classification for Deep Speech Recognition

Machine Learning systems are vulnerable to adversarial attacks and will ...

0 Saeid Samizade, et al. ∙

research

∙ 09/13/2019

Deep Joint Embeddings of Context and Content for Recommendation

This paper proposes a deep learning-based method for learning joint cont...

0 Miklas S. Kristoffersen, et al. ∙

research

∙ 09/03/2019

On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement

Many deep learning-based speech enhancement algorithms are designed to m...

0 Morten Kolbæk, et al. ∙

research

∙ 06/22/2019

Keyword Spotting for Hearing Assistive Devices Robust to External Speakers

Keyword spotting (KWS) is experiencing an upswing due to the pervasivene...

0 Iván López-Espejo, et al. ∙

research

∙ 06/09/2019

rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method

This paper presents an unsupervised segment-based method for robust voic...

0 Zheng-Hua Tan, et al. ∙

research

∙ 05/29/2019

Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect

When speaking in presence of background noise, humans reflexively change...

0 Daniel Michelsanti, et al. ∙

research

∙ 05/11/2019

Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) featur...

0 Achintya kr. Sarkar, et al. ∙

research

∙ 12/12/2018

Subjective Annotations for Vision-Based Attention Level Estimation

Attention level estimation systems have a high potential in many use cas...

0 Andrea Coifman, et al. ∙

research

∙ 11/15/2018

Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems

Humans tend to change their way of speaking when they are immersed in a ...

0 Daniel Michelsanti, et al. ∙

research

∙ 11/15/2018

On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement

Audio-visual speech enhancement (AV-SE) is the task of improving speech ...

0 Daniel Michelsanti, et al. ∙

research

∙ 07/30/2018

The Importance of Context When Recommending TV Content: Dataset and Algorithms

Home entertainment systems feature in a variety of usage scenarios with ...

4 Miklas S. Kristoffersen, et al. ∙

research

∙ 06/21/2018

On the Equivalence between Objective Intelligibility and Mean-Squared Error for Deep Neural Network based Speech Enhancement

Although speech enhancement algorithms based on deep neural networks (DN...

0 Morten Kolbæk, et al. ∙

Zheng-Hua Tan

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