A sparse equivalent source method for near-field acoustic holography

Fernández-Grande, Efrén; Xenaki, Angeliki; Gerstoft, Peter

doi:10.1121/1.4974047

Cited by 115 publications

(58 citation statements)

References 35 publications

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“…Exploiting the underlying sparsity, sparse signal reconstruction improves significantly the resolution in DOA estimation. [9][10][11][12] While ' pnorm regularized maximum likelihood methods, with p 1, have been proposed to promote sparsity in DOA estimation [9][10][11]13 and wavefield reconstruction, 14,15 the accuracy of the resulting sparse estimate is determined by the ad hoc choice of the regularization parameter. 12,16 Sparse Bayesian learning (SBL) is a probabilistic parameter estimation approach which is based on a hierarchical Bayesian method for learning sparse models from possibly overcomplete representations resulting in robust maximum likelihood estimates.…”

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confidence: 99%

Sound source localization and speech enhancement with sparse Bayesian learning beamforming

Xenaki

Boldt

Christensen

2018

The Journal of the Acoustical Society of America

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Speech localization and enhancement involves sound source mapping and reconstruction from noisy recordings of speech mixtures with microphone arrays. Conventional beamforming methods suffer from low resolution, especially with a limited number of microphones. In practice, there are only a few sources compared to the possible directions-of-arrival (DOA). Hence, DOA estimation is formulated as a sparse signal reconstruction problem and solved with sparse Bayesian learning (SBL). SBL uses a hierarchical two-level Bayesian inference to reconstruct sparse estimates from a small set of observations. The first level derives the posterior probability of the complex source amplitudes from the data likelihood and the prior. The second level tunes the prior towards sparse solutions with hyperparameters which maximize the evidence, i.e., the data probability. The adaptive learning of the hyperparameters from the data auto-regularizes the inference problem towards sparse robust estimates. Simulations and experimental data demonstrate that SBL beamforming provides high-resolution DOA maps outperforming traditional methods especially for correlated or non-stationary signals. Specifically for speech signals, the high-resolution SBL reconstruction offers not only speech enhancement but effectively speech separation.

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confidence: 99%

Sound source localization and speech enhancement with sparse Bayesian learning beamforming

Xenaki

Boldt

Christensen

2018

The Journal of the Acoustical Society of America

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“…, A N ] T is the vector of unknown source strengths. In practice, the system of equations (3) is typically underdetermined "since there are often more waves in the model than measurement points" [11]. There are well-known methods for solving such systems of equations [11,12].…”

Section: Free Spacementioning

confidence: 99%

On the use of the equivalent source method for free-field calibration of an acoustic radiator in a reverberant tank

Virovlyansky

Deryabin

2019

Journal of Sound and Vibration

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It is shown that the use of the equivalent source method allows one to reconstruct the sound field excited by an acoustic radiator in free space from the measurements of the sound filed excited by this radiator in a tank with reflecting boundaries. The key assumption underlying the proposed approach is that the same set of monopoles with the same strengths can reproduce the field of the radiator both in free space and in the tank. The monopole strengths are reconstructed from the measurements in the tank, and the field in free space is then calculated using the known Green function. The feasibility of the approach is demonstrated in a tank experiment.

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“…After that, Fernandez-Grande et al applied the CS theory to a wave expansion method based on measurements with a spherical microphone array [17]. E. Fernandez-Grande has presented a detailed study of the compressive ESM (CESM) [18]. When the source is distributed sparsely, the CS theory can be directly applied to NAH to reduce the number of measurement points or to extend the frequency range, which has been intensively studied in many literatures [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Compressed Equivalent Source Method Based on Equivalent Redundant Dictionary for Sound Field Reconstruction

Chen

et al. 2019

Applied Sciences

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The equivalent source method (ESM) based on compressive sensing (CS) requires that the source has a sparse or approximately sparse representation in a suitable basis or dictionary. However, in practical applications, it is not easy to find the appropriate basis or dictionary due to the indeterminate characteristics of the source. To solve this problem, an equivalent redundant dictionary is constructed, which contains two core parts: one is the equivalent dictionary used in the CS-based ESMs under the sparse assumption, and the other one is the orthogonal basis obtained by the singular value decomposition (SVD). On this foundation, a method named compressed ESM based on the equivalent redundant dictionary (ERDCESM) is proposed to enhance the performances of source field reconstruction for different types of sources. Moreover, inspired by the idea of functional beamforming (FB), ERDCESM with order v (ERDCESM- v ) can possess a high dynamic range when detecting the source location. The numerical simulations are carried out at different frequencies to evaluate the performance of the proposed method, and the results suggest that the proposed method performs well both for sparse and even spatially extended sources. The validity and practicality of the proposed method are also verified by the experimental results.

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A sparse equivalent source method for near-field acoustic holography

Cited by 115 publications

References 35 publications

Sound source localization and speech enhancement with sparse Bayesian learning beamforming

Sound source localization and speech enhancement with sparse Bayesian learning beamforming

On the use of the equivalent source method for free-field calibration of an acoustic radiator in a reverberant tank

A Compressed Equivalent Source Method Based on Equivalent Redundant Dictionary for Sound Field Reconstruction

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