We provide a speech coding scheme employing a generative model based on SampleRNN
that, while operating at significantly lower bitrates, matches or surpasses the perceptual …

Neural networks have proven to be a formidable tool to tackle the problem of speech coding
at very low bit rates. However, the design of a neural coder that can be operated robustly …

Speech codecs learn compact representations of speech signals to facilitate data
transmission. Many recent deep neural network (DNN) based end-to-end speech codecs …

Recently, speech codecs based on neural networks have proven to perform better than
traditional methods. However, redundancy in traditional parameter quantization is visible …

In order to efficiently transmit and store speech signals, speech codecs create a minimally
redundant representation of the input signal which is then decoded at the receiver with the …

The recent emergence of machine-learning based generative models for speech suggests a
significant reduction in bit rate for speech codecs is possible. However, the performance of …

This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an
extension of the open-source speech processing toolkit FunASR. FunCodec provides …

This paper introduces a novel neural network-based speech coding system that can process
noisy speech effectively. The proposed source-aware neural audio coding (SANAC) system …

Audio codecs based on discretized neural autoencoders have recently been developed and
shown to provide significantly higher compression levels for comparable quality speech out …

Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to
outperform autoregressive models in perceptual quality with much higher generation speed …