activity
20172022
most citedSpeech Enhancement using a Deep Mixture of Experts

13 citations · 21 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.SD2022

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…

cs.SD20224 cited

CNN self-attention voice activity detector

Amit Sofer, Shlomo E. Chazan

In this work we present a novel single-channel Voice Activity Detector (VAD) approach. We utilize a Convolutional Neural Network (CNN) which exploits the spatial information of the…

cs.SD2022

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…

cs.SD2021

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…

cs.SD2020

Single channel voice separation for unknown number of speakers under reverberant and noisy settings

Shlomo E. Chazan, Lior Wolf, Eliya Nachmani +1

We present a unified network for voice separation of an unknown number of speakers. The proposed approach is composed of several separation heads optimized together with a speaker…

cs.SD201713 cited

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…