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Directly sending audio signals from a transmitter to a receiver across anoisy channel may absorb consistent bandwidth and be prone to errors whentrying to recover the transmitted bits. On the contrary, the recent semanticcommunication approach proposes to send the semantics and then regeneratesemantically consistent content at the receiver without exactly recovering thebitstream. In this paper, we propose a generative audio semantic communicationframework that faces the communication problem as an inverse problem, thereforebeing robust to different corruptions. Our method transmits lower-dimensionalrepresentations of the audio signal and of the associated semantics to thereceiver, which generates the corresponding signal with a particular focus onits meaning (i.e., the semantics) thanks to the conditional diffusion model atits core. During the generation process, the diffusion model restores thereceived information from multiple degradations at the same time includingcorruption noise and missing parts caused by the transmission over the noisychannel. We show that our framework outperforms competitors in a real-worldscenario and with different channel conditions. Visit the project page tolisten to samples and access the code:https://ispamm.github.io/diffusion-audio-semantic-communication/.

Diffusion models for audio semantic communication