# Contrast-PLC: Contrastive Learning for Packet Loss Concealment

arXiv: https://arxiv.org/abs/2302.13284 · HTML: https://mlsanity.com/paper/2302.13284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting](https://mlsanity.com/paper/2405.20101/) (2024)

## Abstract (arXiv)

Packet loss concealment (PLC) is challenging in concealing missing contents both plausibly and naturally when there are only limited available context to use. Recently deep-learning based PLC algorithms have demonstrated their superiority over traditional counterparts; but their concealment ability is still mostly limited to a maximum of 120ms loss. Even with strong GAN-based generative models, it is still very challenging to predict long burst losses that could happen within/in-between phonemes. In this paper, we propose to use contrastive learning to learn a loss-robust semantic representation for PLC. A hybrid neural PLC architecture combining the semantic prediction and GAN-based generative model is designed to verify its effectiveness. Results on the blind test set of Interspeech2022 PLC Challenge show its superiority over commonly used UNet-style framework and the one without contrastive learning, especially for the longer burst loss at (120, 220] ms.