574 citations · 866 across the 29 of their papers we have counts for
32 papers
Comparison of Frequency-Fusion Mechanisms for Binaural Direction-of-Arrival Estimation for Multiple Speakers
Daniel Fejgin, Elior Hadad, Sharon Gannot +2
To estimate the direction of arrival (DOA) of multiple speakers with methods that use prototype transfer functions, frequency-dependent spatial spectra (SPS) are usually constructe…
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
Yochai Yemini, Aviv Shamsian, Lior Bracha +2
Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce h…
A two-stage speaker extraction algorithm under adverse acoustic conditions using a single-microphone
Aviad Eisenberg, Sharon Gannot, Shlomo E. Chazan
In this work, we present a two-stage method for speaker extraction under reverberant and noisy conditions. Given a reference signal of the desired speaker, the clean, but the still…
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
Idan Cohen, Ofir Lindenbaum, Sharon Gannot
Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to re…
Magnitude or Phase? A Two Stage Algorithm for Dereverberation
Ayal Schwartz, Sharon Gannot, Shlomo E. Chazan
In this work we present a new single-microphone speech dereverberation algorithm. First, a performance analysis is presented to interpret that algorithms focused on improving solel…
Single microphone speaker extraction using unified time-frequency Siamese-Unet
Aviad Eisenberg, Sharon Gannot, Shlomo E. Chazan
In this paper we present a unified time-frequency method for speaker extraction in clean and noisy conditions. Given a mixed signal, along with a reference signal, the common appro…
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
Diego Di Carlo, Pinchas Tandeitnik, Cédric Foy +3
This paper presents dEchorate: a new database of measured multichannel Room Impulse Responses (RIRs) including annotations of early echo timings and 3D positions of microphones, re…
Speech enhancement with mixture-of-deep-experts with clean clustering pre-training
Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot
In this study we present a mixture of deep experts (MoDE) neural-network architecture for single microphone speech enhancement. Our architecture comprises a set of deep neural netw…
Semi-supervised source localization in reverberant environments with deep generative modeling
Michael J. Bianco, Sharon Gannot, Efren Fernandez-Grande +1
We propose a semi-supervised approach to acoustic source localization in reverberant environments based on deep generative modeling. Localization in reverberant environments remain…
Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields
Gabriel F Miller, Andreas Brendel, Walter Kellermann +1
In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic en…
Scene-Agnostic Multi-Microphone Speech Dereverberation
Yochai Yemini, Ethan Fetaya, Haggai Maron +1
Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays. Nevertheless, most existing NN architectures can o…
FCN Approach for Dynamically Locating Multiple Speakers
Hodaya Hammer, Shlomo E. Chazan, Jacob Goldberger +1
In this paper, we present a deep neural network-based online multi-speaker localisation algorithm. Following the W-disjoint orthogonality principle in the spectral domain, each tim…
Semi-supervised source localization with deep generative modeling
Michael J. Bianco, Sharon Gannot, Peter Gerstoft
We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAEs). Localization in reverberant environments remains a challe…
MIRaGe: Multichannel Database Of Room Impulse Responses Measured On High-Resolution Cube-Shaped Grid In Multiple Acoustic Conditions
Jaroslav Čmejla, Tomáš Kounovský, Sharon Gannot +2
We introduce a database of multi-channel recordings performed in an acoustic lab with adjustable reverberation time. The recordings provide information about room impulse responses…
ML Estimation and CRBs for Reverberation, Speech and Noise PSDs in Rank-Deficient Noise-Field
Yaron Laufer, Bracha Laufer-Goldshtein, Sharon Gannot
Speech communication systems are prone to performance degradation in reverberant and noisy acoustic environments. Dereverberation and noise reduction algorithms typically require s…
Machine learning in acoustics: theory and applications
Michael J. Bianco, Peter Gerstoft, James Traer +4
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transforma…
Binaural LCMV Beamforming with Partial Noise Estimation
Nico Gößling, Elior Hadad, Sharon Gannot +1
Besides reducing undesired sources (interfering sources and background noise), another important objective of a binaural beamforming algorithm is to preserve the spatial impression…
Multichannel Online Dereverberation based on Spectral Magnitude Inverse Filtering
Xiaofei Li, Laurent Girin, Sharon Gannot +1
This paper addresses the problem of multichannel online dereverberation. The proposed method is carried out in the short-time Fourier transform (STFT) domain, and for each frequenc…
Deep Clustering Based on a Mixture of Autoencoders
Shlomo E. Chazan, Sharon Gannot, Jacob Goldberger
In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clus…
Speech Dereverberation Using Fully Convolutional Networks
Ori Ernst, Shlomo E. Chazan, Sharon Gannot +1
Speech derverberation using a single microphone is addressed in this paper. Motivated by the recent success of the fully convolutional networks (FCN) in many image processing appli…
Data-Driven Source Separation Based on Simplex Analysis
Bracha Laufer-Goldshtein, Ronen Talmon, Sharon Gannot
Blind source separation (BSS) is addressed, using a novel data-driven approach, based on a well-established probabilistic model. The proposed method is specifically designed for se…
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function
Xiaofei Li, Laurent Girin, Sharon Gannot +1
This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, \emph{assuming known mixing filters}. We propose to perform…
Speech Enhancement using a Deep Mixture of Experts
Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot
In this study we present a Deep Mixture of Experts (DMoE) neural-network architecture for single microphone speech enhancement. By contrast to most speech enhancement algorithms th…
Multiple-Speaker Localization Based on Direct-Path Features and Likelihood Maximization with Spatial Sparsity Regularization
Xiaofei Li, Laurent Girin, Sharon Gannot +1
This paper addresses the problem of multiple-speaker localization in noisy and reverberant environments, using binaural recordings of an acoustic scene. A Gaussian mixture model (G…