论文标题
关于深层的语音数据包丢失隐藏：迷你调查
On Deep Speech Packet Loss Concealment: A Mini-Survey
论文作者
论文摘要
数据包损失是数据传输中的一个常见问题，使用IP上的语音。这个问题是一个古老的问题，并且已经开发出了各种经典方法来克服这个问题。但是，随着深度学习和生成模型的兴起，例如生成的对抗网络和自动编码器，已经出现了一种新的途径，试图通过为丢失的数据包生成替代品来尝试使用深度学习来解决数据包损失。在这个迷你调查中，我们回顾了我们迄今为止发现的所有文献，这些文献试图使用深度学习方法在语音中解决数据包损失。此外，我们简要介绍如何在现实环境中建模数据包损失问题，以及如何评估数据包丢失隐藏技术。此外，我们回顾了相关领域中一些现代的深度学习技术，这些技术显示出令人鼓舞的结果。这些技术阐明了未来对PLC的更好解决方案以及需要与数据包损害同时考虑的其他挑战。
Packet-loss is a common problem in data transmission, using Voice over IP. The problem is an old problem, and there has been a variety of classical approaches that were developed to overcome this problem. However, with the rise of deep learning and generative models like Generative Adversarial Networks and Autoencoders, a new avenue has emerged for attempting to solve packet-loss using deep learning, by generating replacements for lost packets. In this mini-survey, we review all the literature we found to date, that attempt to solve the packet-loss in speech using deep learning methods. Additionally, we briefly review how the problem of packet-loss in a realistic setting is modelled, and how to evaluate Packet Loss Concealment techniques. Moreover, we review a few modern deep learning techniques in related domains that have shown promising results. These techniques shed light on future potentially better solutions for PLC and additional challenges that need to be considered simultaneously with packet-loss.
