The acoustic inverse problem in the framework of alternating direction method of multipliers

Spatial basis interpretation for implementing the acoustic imaging of non-synchronous measurements

Achieving the sparse acoustical holography via the sparse bayesian learning

An improvement of the generalized discrete Fourier series based patch near-field acoustical holography

Nearfield acoustic holography based on the compressed sensing theory can realize the accurate reconstruction of sound fields with fewer measurement points on the premise that an appropriate sparse basis is obtained. However, for different types of sound sources, the appropriate sparse bases are diverse and should be constructed elaborately. In this paper, a block sparse Bayesian learning (SBL) equivalent source method is proposed for realizing the reconstruction of the sound fields radiated by different types of sources, including the spatially sparse sources, the spatially extended sources, and the mixed ones of the above two, without the elaborate construction of the sparse basis. The proposed method constructs a block sparse equivalent source model and promotes a block sparse solution by imposing a structured prior on the equivalent source model and estimating the posterior of the model by using the SBL, which can achieve the accurate reconstruction of the radiated sound fields of different types of sources simply by adjusting the block size. Numerical simulation and experimental results demonstrate the validity and superiority of the proposed method, and the effects of two key parameters, the block size, and sparsity pruning threshold value are investigated through simulations.

Sound field reconstruction using block sparse Bayesian learning equivalent source method.

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed one has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, as it is developed in the framework of multitask compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

Fast sparse reconstruction of sound field via Bayesian compressive sensing

The invention relates to a sound field separation method adopting sparse measurement, and belongs to the technical field of acoustics. The method has the characteristics of arranging two measuring surfaces between sound sources of a plurality of to-be-separated sound fields, using a random sparse array to measure sound pressure signal on the measuring surfaces, arranging an equivalent sound sourceface which is provided with point sound sources on two sides of the measuring surface, and fitting the sound pressure signal with a point sound source according to the equivalent source principle andthe wave superposition principle to form a fitting sound field; obtaining a sparse basis of a fitting sound field sound pressure vector through a singular value decomposition according to the compressed sensing theory, and establishing a sound field separation model; and substituting the measured sound pressure signal and the sparse basis of the fitting sound field sound pressure vector into thesound field separation model, obtaining a sparse solution of a weight vector through a sparse regularization method, so as to separately obtaining sound fields generated by sound sources at two sidesof the measuring surface to achieve sound field separation. According to the sound field separation method adopting sparse measurement, only fewer measuring points are needed to perform sound field separation on the sound sources, and the method has high separation precision and spatial resolution.

Fang Yu

Papers

Fast sparse reconstruction of sound field via Bayesian compressive sensing

Sound field separation method adopting sparse measurement