Sound field recovery based on numerical data-driven and equivalent source method

Sparse representation has been applied to realize an accurate description of the sound field from limited set of measurements. The prerequisite of this application is that sound field can be sparsely represented in a specific basis. However, most of the sparse bases are constructed by using the physical models of sound field, and are only effective for a specific category of sound sources. In this paper, the data-driven dictionary learning approach is exploited to obtain a sparse basis of sound field. Meanwhile, to reduce the difficulty and workload of the collection of data sample, the equivalent source method is utilized to collect data samples by means of simulations and thus the data samples can be generated by taking advantage of sound field properties. The performance of the sound field recovery with sparse sampling based on learned dictionary is examined and is compared with those based on other sparse bases. The result indicates that numerical data-driven model is more flexible and is not limited to a specific category of sound sources. The advantage of the proposal is presented by the results of numerical simulation and experiment.