  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/.
