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20162026
most citedRobust Sound Source Localization Using a Microphone Array on a Mobile Robot

383 citations · 1.4k across the 49 of their papers we have counts for

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Showing 2022 · eess.ASShow all

8 papers · 2 filters

eess.AS2022

Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity

Ahmed Mustafa, Jean-Marc Valin, Jan Büthe +2

GAN vocoders are currently one of the state-of-the-art methods for building high-quality neural waveform generative models. However, most of their architectures require dozens of b…

eess.AS2022

DRED: Deep REDundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder

Jean-Marc Valin, Jan Büthe, Ahmed Mustafa +1

Despite recent advancements in packet loss concealment (PLC) using deep learning techniques, packet loss remains a significant challenge in real-time speech communication. Redundan…

eess.AS2022★ 7 cited

Semi-supervised Time Domain Target Speaker Extraction with Attention

Zhepei Wang, Ritwik Giri, Shrikant Venkataramani +5

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on…

eess.AS2022★ 2 cited

To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets

Jean-Marc Valin, Ritwik Giri, Shrikant Venkataramani +2

In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech. Recently, deep learning-based speech enhancement approa…

eess.AS2022★ 1 cited

Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model

Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery +4

As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, atten…

eess.AS2022

End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation

Krishna Subramani, Jean-Marc Valin, Umut Isik +2

Neural vocoders have recently demonstrated high quality speech synthesis, but typically require a high computational complexity. LPCNet was proposed as a way to reduce the complexi…

eess.AS2022

Improved singing voice separation with chromagram-based pitch-aware remixing

Siyuan Yuan, Zhepei Wang, Umut Isik +4

Singing voice separation aims to separate music into vocals and accompaniment components. One of the major constraints for the task is the limited amount of training data with sepa…

eess.AS2022

Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet

Jean-Marc Valin, Umut Isik, Paris Smaragdis +1

Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so. In previous work, we introduced LPCNet, which uses…