Packet Loss Concealment

Packet loss concealment (PLC) aims to restore lost audio or speech data in real-time communication systems, mitigating the impact of network interruptions on quality. Current research heavily utilizes deep learning, employing architectures like generative adversarial networks (GANs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), often incorporating multi-task learning and contrastive learning techniques to improve concealment quality and robustness. These advancements are crucial for enhancing the reliability and user experience of applications like VoIP and networked music performance, driving ongoing efforts to develop more accurate objective evaluation metrics and improve real-time performance.