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