A data-driven sparse recovery method for surface velocity of the sound source
The accurate description of surface velocity of the sound source is of great significance.The accuracy of the description of velocity mainly depends on the number of sampling points,and the increasing number of sampling points leads to the high cost of measurement.To overcome the aforementioned issue,a data-driven sparse recovery method for surface velocity of the sound source is proposed in this study.In the method,the velocity data sample is generated by numerical simulations by taking advantage of equivalent source method.Then the sparse basis of surface velocity of the sound source is constructed based on K-SVD dictionary learning method and sparse regularization is applied to realize an accurate reconstruction of the surface velocity with limited number of sampling points.To validate the effectiveness of the proposed method,the simulation of simply supported plate is conducted and the experiment is carried out in the anechoic chamber.The results of the simulation and the experiment indicate that compared with the conventional equivalent source method,the proposed method provides a more accurate reconstruction of the surface velocity.Meanwhile,the performance of the proposed method is more stable,which can provide a new scheme for the measurement of the surface velocity of the sound source.
surface velocity of the sound sourcevelocity recoverydata-drivendictionary learningequivalent source method
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