activity
20112024
most citedHand-Eye Calibration

592 citations · 2.3k across the 24 of their papers we have counts for

collaborators
Showing cs.SDShow all

12 papers · 1 filter

cs.SD2022

The impact of removing head movements on audio-visual speech enhancement

Zhiqi Kang, Mostafa Sadeghi, Radu Horaud +3

This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by…

cs.SD202027 cited

Reverberant Sound Localization with a Robot Head Based on Direct-Path Relative Transfer Function

Xiaofei Li, Laurent Girin, Fabien Badeig +1

This paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in loca…

cs.SD2019

Narrow-band Deep Filtering for Multichannel Speech Enhancement

Xiaofei LI, Radu Horaud

In this paper, we address the problem of multichannel speech enhancement in the short-time Fourier transform (STFT) domain. A long short-time memory (LSTM) network takes as input a…

cs.SD2019

Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders

Mostafa Sadeghi, Simon Leglaive, Xavier Alameda-PIneda +2

Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to lear…

cs.SD20194 cited

Expectation-Maximization for Speech Source Separation Using Convolutive Transfer Function

Xiaofei Li, Laurent Girin, Radu Horaud

This paper addresses the problem of under-determinded speech source separation from multichannel microphone singals, i.e. the convolutive mixtures of multiple sources. The time-dom…

cs.SD201944 cited

Speech enhancement with variational autoencoders and alpha-stable distributions

Simon Leglaive, Umut Simsekli, Antoine Liutkus +2

This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders…