Passive acoustic monitoring provides continuous, high-resolution recordings, but real ocean soundscapes are messy: noise is non-stationary and biologically important click events can be sparse. These conditions often challenge purely discriminative detectors. This poster explores Information Field Theory (IFT) as a Bayesian alternative for acoustic denoising. IFT treats signals as latent fields and reconstructs their posterior mean together with calibrated uncertainty.
Methodologically, the spectrogram is modeled as data generated from a latent acoustic field plus noise. A time-periodic, frequency-random prior encodes expected click structure, and variational optimisation of the Gibbs free energy yields the reconstruction. Minimising this free energy corresponds to variational Bayesian inference within IFT.
Presented at HAICON25, Karlsruhe (03.06.2025) — Schmieder J., Albrecht S., Mousavi H., Fais A.