In this paper, we propose "SoundSpring", a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.

翻译：本文提出"SoundSpring"——一种先进的容错音频收发器，它融合了联合信源信道编码（JSCC）的鲁棒性优势，同时兼容现有数字通信系统。与近期通过神经网络直接将音频信号映射为模拟信道输入符号的深度JSCC收发器不同，我们的SoundSpring采用分层架构，将音频压缩与数字编码传输分离，但充分挖掘了大语言（基础）模型卓越的上下文预测能力。结合因果序掩码学习策略，我们的单一模型在潜在特征域运行并实现双功能：在发射端作为高效音频压缩器，在接收端作为有效的丢包隐藏机制。通过联合优化音频压缩效率与传输容错性，我们证明掩码学习语言模型确实是强大的上下文预测器，而我们的双功能压缩与隐藏框架为基础语言模型在音频通信中的应用提供了新视角。通过大量实验评估，我们证实SoundSpring在信号保真度指标和感知质量评分上显著优于当代音频传输系统。这些新发现不仅支持SoundSpring在基于学习的音频通信系统中的实际部署，也为未来音频语义收发器的发展提供了启示。