Directly measuring the full set of acoustic impulse responses within a room would require an unreasonably large number
of measurements. Considering that the acoustic wavefield is sparse in some dictionaries, Compressed Sensing allows the
recovery of the full wavefield with a reduced set of measurements, but raises challenging computational and memory issues.
Two practical algorithms are presented and compared: one that exploits the structured sparsity of the soundfield, with
projections of the modes onto plane waves sharing the same wavenumber, and one that computes a sparse decomposition
on a dictionary of independent plane waves with time/space variable separation.
We study local deformations of time-frequency and time-scale representations, in the framework of the so-called reassignment methods, which aim at `deblurring' time- frequency representations. We focus on deformations generated by appropriate vector fields defined on time- frequency or time scale plane, and constructed on the basis of geometric and group-theoretical arguments. Such vector fields may be used as such for signal analysis (as quantities generalizing instantaneous frequency or group delay) in the framework of reassignment algorithms.
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