Compressed sensing of impulse responses in rooms of unknown properties and contents

Zea, Elías

doi:10.1016/j.jsv.2019.114871

Cited by 10 publications

(40 citation statements)

References 51 publications

(80 reference statements)

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“…Several attempts have been made in the literature for RIR interpolation and sound field reconstruction, such as dynamic time warping [1,2], parametric approaches [3,4], compressive sensing [5,6,7], spherical harmonics [8], physics-based methods [9,10,11] and more recently, neural networks [12]. In this paper, we extend the common-acoustical pole and residue model proposed by Haneda et al in [13].…”

Section: Introductionmentioning

confidence: 94%

Room Impulse Response Interpolation from a Sparse Set of Measurements Using a Modal Architecture

Das

Calamia

Garí

2021

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

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In augmented reality applications, where room geometries and material properties are not readily available, it is desirable to get a representation of the sound field in a room from a limited set of available room impulse response measurements. In this paper, we propose a novel method for 2D interpolation of room modes from a sparse set of RIR measurements that are non-uniformly sampled within a space. We first obtain the mode parameters of a measured room. Using the commonacoustical pole theory, the mode frequencies and decay rates are kept constant over space, and a unique set of mode amplitudes is obtained for each measurement location. Based on the general solution to the Helmholtz equation, these mode amplitudes are modeled as periodic functions of 2D spatial location. For low frequency room modes, the model parameters are found with sequential non-linear least squares. Results show accurate spatial interpolation of perceptually relevant low frequency modes in rooms with simple geometries having non-rigid walls.

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Section: Introductionmentioning

confidence: 94%

Room Impulse Response Interpolation from a Sparse Set of Measurements Using a Modal Architecture

Das

Calamia

Garí

2021

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

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“…The goal is either the interpolation or the extrapolation of RIRs related to locations different from those in which physical microphones or loudspeakers are placed. We can mainly identify two categories of sound field reconstruction solutions: parametric methods [ 11 , 12 , 13 , 14 , 15 ] and non-parametric [ 16 , 17 , 18 , 19 , 20 , 21 ] techniques. On the one hand, parametric solutions [ 11 , 12 , 13 , 14 , 15 ] rely on simplified parametric models of the sound field.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the target of these techniques is to convey an effective spatial audio perception to the user rather than a perfect reconstruction of the sound field. On the other hand, non-parametric methods [ 16 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] aim to numerically estimate the acoustic field. In this category, the sound field is typically modelled as a linear combination of solutions of the wave equation [ 27 ], e.g., plane wave [ 25 , 26 ] or spherical wave [ 22 , 23 , 24 , 28 , 29 ] expansions.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 20 ], compressed sensing is employed to directly exploit the inherent signal structure of the RIRs without adding further assumptions. As a matter of fact, the mathematical structure of the wavefronts provides all the information related to the sound propagation in the environment.…”

Section: Introductionmentioning

confidence: 99%

“…As a matter of fact, the mathematical structure of the wavefronts provides all the information related to the sound propagation in the environment. This enables in [ 20 ] the interpolation of RIRs in a Uniform Linear Array (ULA). The same strategy was previously adopted for interpolating seismic traces [ 32 ], a problem akin to the reconstruction of RIRs since it also concerns the recovery of propagating wavefronts in a medium.…”

Section: Introductionmentioning

confidence: 99%

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Deep Prior Approach for Room Impulse Response Reconstruction

Pezzoli

Perini

Bernardini

et al. 2022

Sensors

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In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.

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Sparse Recovery of Sound Fields Using Measurements from Moving Microphones

Katzberg

Mertins

2022

Applied and Numerical Harmonic Analysis

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Compressed sensing of impulse responses in rooms of unknown properties and contents

Cited by 10 publications

References 51 publications

Room Impulse Response Interpolation from a Sparse Set of Measurements Using a Modal Architecture

Room Impulse Response Interpolation from a Sparse Set of Measurements Using a Modal Architecture

Deep Prior Approach for Room Impulse Response Reconstruction

Sparse Recovery of Sound Fields Using Measurements from Moving Microphones

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