Directly sending audio signals from a transmitter to a receiver across a noisy channel may absorb consistent bandwidth and be prone to errors when trying to recover the transmitted bits. On the contrary, the recent semantic communication approach proposes to send the semantics and then regenerate semantically consistent content at the receiver without exactly recovering the bitstream. In this paper, we propose a generative audio semantic communication framework that faces the communication problem as an inverse problem, therefore being robust to different corruptions. Our method transmits lower-dimensional representations of the audio signal and of the associated semantics to the receiver, which generates the corresponding signal with a particular focus on its meaning (i.e., the semantics) thanks to the conditional diffusion model at its core. During the generation process, the diffusion model restores the received information from multiple degradations at the same time including corruption noise and missing parts caused by the transmission over the noisy channel. We show that our framework outperforms competitors in a real-world scenario and with different channel conditions. Visit the project page to listen to samples and access the code: https://ispamm.github.io/diffusion-audio-semantic-communication/.

翻译：直接通过噪声信道将音频信号从发送端传输至接收端，在尝试恢复传输比特时会消耗大量带宽且容易出错。相比之下，近年来提出的语义通信方法主张仅传输语义信息，并在接收端重新生成语义一致的内容，无需精确还原比特流。本文提出一种生成式音频语义通信框架，该框架将通信问题视为逆问题，因此对不同类型的干扰具有鲁棒性。我们的方法向接收端传输音频信号及其关联语义的低维表示，接收端借助核心条件扩散模型，在生成对应信号时特别关注其意义（即语义）。在生成过程中，扩散模型同时从多重退化中恢复接收信息，包括噪声信道传输导致的污染噪声和缺失片段。实验表明，本框架在真实场景及不同信道条件下均优于现有方法。请访问项目页面收听示例音频并获取代码：https://ispamm.github.io/diffusion-audio-semantic-communication/.