Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field

Antonello, Niccolò; Sena, Enzo De; Moonen, Marc; Naylor, Patrick A.; Waterschoot, Toon van

doi:10.1109/taslp.2017.2730284

Cited by 54 publications

(56 citation statements)

References 35 publications

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“…The choice of these sparsity promoting regularization terms is motivated by the fact that P e , consists of a sparse set of plane waves arriving from a limited number of directions. However, (19) aims at reconstructing the whole sound field P, which due to the presence of the diffuse field component P d is not a sparse set of plane waves. The parameter should be tuned such that both P e and P d are jointly reconstructed with accuracy while preserving a sufficient level of sparsity to enable joint localization and dereverberation.…”

Section: Acoustic Model a Plane Wave Is Defined Asmentioning

confidence: 99%

“…Here the SHDM is used to construct an over-complete dictionary that accounts for the presence of a rigid baffle of a spherical microphone array. Group sparsity has also been proposed to model sound fields leading to spatial, spatio-temporal and spatio-spectral sparsity [19], [20] and has been shown to improve DOA estimation particularly when combined with speech modeling [21]. Similar approaches have been used also for a dereverberation task.…”

mentioning

confidence: 99%

“…In this paper, a recently proposed RIR interpolation algorithm [19], is reformulated and modified to be able to perform joint source localization and dereverberation. The proposed algorithm, called acoustic dereverberation and localization through field approximation (ADeLFA), relies on the approximation of the sound field recorded by a set of microphones which is formulated as a regularized inverse problem.…”

mentioning

confidence: 99%

“…On the other hand, spatio-spectral sparsity lead to weight signals that have a spectrum composed by few frequency components. Notice that in [19] spatio-temporal sparsity is promoted as well by employing a different acoustic model named timedomain equivalent source method (TESM). Being a timedomain method, TESM can easily allow for the promotion spatio-temporal sparsity.…”

mentioning

confidence: 99%

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Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

Antonello

Sena

Moonen

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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Acoustic source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists of the approximation of the sound field measured by a set of microphones. The recorded sound pressure is matched with that of a particular acoustic model based on a collection of plane waves arriving from different directions at the microphone positions. In order to achieve meaningful results, spatial and spatio-spectral sparsity can be promoted in the weight signals controlling the plane waves. The large-scale optimization problem resulting from the inverse problem formulation is solved using a first order optimization algorithm combined with a weighted overlap-add procedure. It is shown that once the weight signals capable of effectively approximating the sound field are obtained, they can be readily used to localize a moving sound source in terms of direction of arrival (DOA) and to perform dereverberation in a highly reverberant environment. Results from simulation experiments and from real measurements show that the proposed algorithm is robust against both localized and diffuse noise exhibiting a noise reduction in the dereverberated signals.

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Section: Acoustic Model a Plane Wave Is Defined Asmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

Antonello

Sena

Moonen

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Besides sparse plane wave representation an interesting sparse approach to the estimation of RTF is a recent approach with orthonormal basis functions based on infinite impulse response filters (IIR) [9]. Though not exploring plane wave sparsity, the solution relying on the weighted spatio-temporal representation [10] also gives promising room impulse response interpolations.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of the Room Geometry and Modes with Compressed Sensing

Tukuljac

Lissek

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room requires a large number of measurements. In order to lower the required sampling rate, some solutions have emerged that exploit the sparse representation of the room wavefield in the terms of plane waves in the lowfrequency domain. The plane wave representation has a simple form in rectangular rooms. In our solution, we observe the basic axial modes of the wave vector grid for extraction of the room geometry and then we propagate the knowledge to higher order modes out of the low-pass version of the measurements. Estimation of the approximate structure of the kspace should lead to the reduction in the terms of number of required measurements and in the increase of the speed of the reconstruction without great losses of quality.

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Sparse Recovery of Sound Fields Using Measurements from Moving Microphones

Katzberg

Mertins

2022

Compressed Sensing in Information Processing

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Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field

Cited by 54 publications

References 35 publications

Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

Joint Estimation of the Room Geometry and Modes with Compressed Sensing

Sparse Recovery of Sound Fields Using Measurements from Moving Microphones

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