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

By Jean-Marc Valin, Jan Büthe, Ahmed Mustafa, Michael Klingbeil. 2022

10 citations recorded by the source corpus.

Despite recent advancements in packet loss concealment (PLC) using deep learning techniques, packet loss remains a significant challenge in real-time speech communication. Redundancy has been used in the past to recover the missing information during losses. However, conventional redundancy techniques are limited in the maximum loss duration they can cover and are often unsuitable for burst packet loss. We propose a new approach based on a rate-distortion-optimized variational autoencoder (RDO-VAE), allowing us to optimize a deep speech compression algorithm for the task of encoding large amounts of redundancy at very low bitrate. The proposed Deep REDundancy (DRED) algorithm can transmit up to 50x redundancy using less than 32 kb/s. Results show that DRED outperforms the existing Opus codec redundancy. We also demonstrate its benefits when operating in the context of WebRTC.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhihao Du
2. Shiliang Zhang
3. Kai Hu
4. Siqi Zheng

In Computer Vision and Pattern Recognition

1. Shiliang Zhang
2. Liang Zheng
3. Yi Yang
4. Qi Tian

In Signal Processing

1. Zhihao Du
2. Shiliang Zhang
3. Kai Hu
4. Siqi Zheng

Citations in the corpus

• FunCodec: A Fundamental, Reproducible and Integrable Open-Source Toolkit for Neural Speech Codec (2023, background): “Following this line of work, extensive efforts have been made on reducing the bit rate [11–13].”