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Found 8 papers, 2 papers with code
NoLACE: Improving Low-Complexity Speech Codec Enhancement Through Adaptive Temporal Shaping
Speech codec enhancement methods are designed to remove distortions added by speech codecs.
LACE: A light-weight, causal model for enhancing coded speech through adaptive convolutions
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.
Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity
GAN vocoders are currently one of the state-of-the-art methods for building high-quality neural waveform generative models.
Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder
Robustness to packet loss is one of the main ongoing challenges in real-time speech communication.
Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model
As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, attention is turning to the problem of packet loss concealment (PLC).
A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate
Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to outperform autoregressive models in perceptual quality with much higher generation speed.
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech.
Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding
Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate.
