Remi Gribonval sent me the following the other day:
Hi Igor,Since you are keen on promoting reproducible research, I thought you might be interested in knowing that we have just released the code of NACHOS, to reproduce the Compressive Acoustic Imaging results of the paper:Gilles Chardon, Laurent Daudet, Antoine Peillot, François Ollivier, Nancy Bertin, Rémi Gribonva l.Nearfield Acoustic Holography using sparsity and compressive sampling principles. Journal of the Acoustical Society of America, 2012.
The code comes with experimental data under a creative commons license. More information atCheers,Remi.
Thanks Remi. I actually am not keen on promoting reproducible research, rather I care about you, your co-authors or anybody releasing an implementation of their work for that matter, becoming rock stars. It's not the same thing.
Here is the attendant paper:
Nearfield Acoustic Holography using sparsity and compressive sampling principles by Gilles Chardon, Laurent Daudet, Antoine Peillot, François Ollivier, Nancy Bertin, Remi Gribonval
Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify the vibrating sources. This paper shows that, for convex homogeneous plates with arbitrary boundary conditions, new regularization schemes can be developed, based on the sparsity of the normal velocity of the plate in a well-designed basis, i.e. the possibility to approximate it as a weighted sum of few elementary basis functions. In particular, these new techniques can handle discontinuities of the velocity field at the boundaries, which can be problematic with standard techniques. This comes at the cost of a higher computational complexity to solve the associated optimization problem, though it remains easily tractable with out-of-the-box software. Furthermore, this sparsity framework allows us to take advantage of the concept of Compressive Sampling: under some conditions on the sampling process (here, the design of a random array, which can be numerically and experimentally validated), it is possible to reconstruct the sparse signals with significantly less measurements (i.e., microphones) than classically required. After introducing the different concepts, this paper presents numerical and experimental results of NAH with two plate geometries, and compares the advantages and limitations of these sparsity-based techniques over standard Tikhonov regularization.The NACHOS download page is here.
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