A Compressed Sensing Framework for Dynamic Sound-Field Measurements

Katzberg, Fabrice; Mazur, Radoslaw; Maass, Marco; Koch, Philipp; Mertins, Alfred

doi:10.1109/taslp.2018.2851144

Cited by 13 publications

(19 citation statements)

References 47 publications

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“…For the recordings, white Gaussian measurement noise was added at signal-to-noise ratios (SNRs) of 10 dB, 20 dB, and 30 dB, respectively. To evaluate spatial RIR recovery, we use the normalized system misalignment (NSM) as in [23,24], which measures the energy ratio between the error signal and the corresponding true RIR. Accordingly, the mean NSM (MNSM) is used to describe the reconstruction error over Ω.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The high number of parameters for D = 3 and the dynamic-sample representation in terms of spatially concentrated, fast decaying interpolation filters most likely lead to ill-posed or even underdetermined problems in practical applications. The solution to this issues is the compressedsensing based recovery as proposed in [24]. This involves addition sparsity constraints and sampling conditions.…”

Section: Interpretation and Comparisonmentioning

confidence: 99%

“…More recently, several methods have been proposed that interpret the reconstruction from dynamic sound-field sampling as spatial interpolation task [19][20][21][22][23][24]. They explicitly use a multidimensional measurement model where a moving microphone simultaneously generates uniform samples in time domain and, in general, non-uniform samples at varying points in the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

“…This method, simplifying the problem to pure interpolation in space, has been investigated for reconstructing binaural impulse responses [21] and RIRs along a circle [22]. In [23,24], a versatile framework has been introduced, where the deconvolution problem in time domain and the interpolation problem in space domain are both integrated into one structured system of linear equations whose solution allows for RIR reconstruction in three-dimensional space. To achieve this, the sound field is parameterized by modeling notional grid points in space and dynamic samples are understood as the result of bandlimited interpolation on that grid using sampled sinc-function approximations.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we first present a more general formulation of the dynamic model proposed in [23,24]. The linear equations are construed in terms of sampling of weighted spatial basis functions, where the weights encode the spatio-temporal RIR and are referred to as sound-field parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spherical Harmonic Representation for Dynamic Sound-Field Measurements

Katzberg

Maass

Mertins

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Continuously moving microphones produce a high number of spatially dense sound-field samples with low effort in hardware and acquisition time. By interpreting the dynamic procedure as the nonuniform sampling of spatial basis functions, a system of linear equations can be set up. Its solution encodes sound-field parameters that allow for the spatio-temporal reconstruction within the measurement area at bandwidths where static methods would require impractical setups. An existing framework considers such basis functions from a signal processing point of view. It uses sinc-function based interpolation filters which are highly localized around sampled trajectories and may lead to ill-posed problems unless sparsity constraints are made, especially for locations that are away from microphone trajectories. In this paper, we present a new physical interpretation of the dynamic sampling problem. Transferring the problem into frequency domain, we describe samples of a moving microphone in terms of sampled spherical harmonic functions. The use of these global basis functions leads to dynamic measurements that inherently encode expanded sound-field information and, thus, allow for robust reconstruction at off-trajectory positions.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Interpretation and Comparisonmentioning

confidence: 99%