1 citations · 2 across the 9 of their papers we have counts for
9 papers
Very Low Complexity Speech Synthesis Using Framewise Autoregressive GAN (FARGAN) with Pitch Prediction
Jean-Marc Valin, Ahmed Mustafa, Jan Büthe
Neural vocoders are now being used in a wide range of speech processing applications. In many of those applications, the vocoder can be the most complex component, so finding lower…
NoLACE: Improving Low-Complexity Speech Codec Enhancement Through Adaptive Temporal Shaping
Jan Büthe, Ahmed Mustafa, Jean-Marc Valin +2
Speech codec enhancement methods are designed to remove distortions added by speech codecs. While classical methods are very low in complexity and add zero delay, their effectivene…
LACE: A light-weight, causal model for enhancing coded speech through adaptive convolutions
Jan Büthe, Jean-Marc Valin, Ahmed Mustafa
Classical speech coding uses low-complexity postfilters with zero lookahead to enhance the quality of coded speech, but their effectiveness is limited by their simplicity. Deep Neu…
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…
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…
A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate
Ahmed Mustafa, Jan Büthe, Srikanth Korse +3
Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to outperform autoregressive models in perceptual quality with much higher generation speed. However,…
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
Ahmed Mustafa, Nicola Pia, Guillaume Fuchs
In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech. Computationally heavy models…
Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding
Ahmed Mustafa, Arijit Biswas, Christian Bergler +2
Classical parametric speech coding techniques provide a compact representation for speech signals. This affords a very low transmission rate but with a reduced perceptual quality o…