In this paper, we propose a modification of the standard Equivalent Source Method (ESM) for Near-Field Acoustic Holography (NAH). As in EMS, we aim at modeling the acoustic pressure radiated from a vibrating object, and its surface velocity, as the joint effect of a set of equivalent sources located within or close to the object itself. The estimation of the equivalent source strengths (weigths) comes from the solution of a highly ill-conditioned problem. Rather than solving this problem in the least-squares sense, we exploit the 3D model of the vibrating object, along with a rough estimate of its physical parameters, to restrict the space of the solutions. More specifically, we make use of Finite Element Analysis for populating a compressed dictionary of possible equivalent source weights. NAH is then approached by seeking a sparse linear combination of the entries of the dictionary. Experiments carried on a public database prove the effectiveness of the proposed technique, especially when the number of available microphones is limited, and in the presence of a significant level of measurement noise.
Dictionary-based Equivalent Source Method for Near-Field Acoustic Holography
Canclini, Antonio;Antonacci, Fabio;Sarti, Augusto
2017-01-01
Abstract
In this paper, we propose a modification of the standard Equivalent Source Method (ESM) for Near-Field Acoustic Holography (NAH). As in EMS, we aim at modeling the acoustic pressure radiated from a vibrating object, and its surface velocity, as the joint effect of a set of equivalent sources located within or close to the object itself. The estimation of the equivalent source strengths (weigths) comes from the solution of a highly ill-conditioned problem. Rather than solving this problem in the least-squares sense, we exploit the 3D model of the vibrating object, along with a rough estimate of its physical parameters, to restrict the space of the solutions. More specifically, we make use of Finite Element Analysis for populating a compressed dictionary of possible equivalent source weights. NAH is then approached by seeking a sparse linear combination of the entries of the dictionary. Experiments carried on a public database prove the effectiveness of the proposed technique, especially when the number of available microphones is limited, and in the presence of a significant level of measurement noise.File | Dimensione | Formato | |
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