Geometrically Constrained Room Modeling With Compact Microphone Arrays

Ribeiro, Flavio; Florêncio, Dinei; Ba, D.E.; Cha, Zhang

doi:10.1109/tasl.2011.2180897

Cited by 49 publications

(52 citation statements)

References 16 publications

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“…Unfortunately [14], does not allow us to specify the ambient dimension of the point set. Imposing this constraint leads to even more dependencies between the matrix elements, and the resulting set of matrices is no longer a cone (it is actually not convex anymore).…”

Section: Practical Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Acoustic echoes reveal room shape

Dokmanić

Parhizkar

Walther

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

224

230

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Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room's response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? We show how to compute the shape of a convex polyhedral room from its response to a known sound, recorded by a few microphones. Geometric relationships between the arrival times of echoes enable us to "blindfoldedly" estimate the room geometry. This is achieved by exploiting the properties of Euclidean distance matrices. Furthermore, we show that under mild conditions, first-order echoes provide a unique description of convex polyhedral rooms. Our algorithm starts from the recorded impulse responses and proceeds by learning the correct assignment of echoes to walls. In contrast to earlier methods, the proposed algorithm reconstructs the full 3D geometry of the room from a single sound emission, and with an arbitrary geometry of the microphone array. As long as the microphones can hear the echoes, we can position them as we want. Besides answering a basic question about the inverse problem of room acoustics, our results find applications in areas such as architectural acoustics, indoor localization, virtual reality, and audio forensics. room geometry | geometry reconstruction | echo sorting | image sources I n a famous paper (1), Mark Kac asks the question "Can one hear the shape of a drum?" More concretely, he asks whether two membranes of different shapes necessarily resonate at different frequencies.* This problem is related to a question in astrophysics (2), and the answer turns out to be negative: Using tools from group representation theory, Gordon et al. (3,4) presented several elegantly constructed counterexamples, including the two polygonal drum shapes shown in Fig. 1. Although geometrically distinct, the two drums have the same resonant frequencies. † In this work, we ask a similar question about rooms. Assume you are blindfolded inside a room; you snap your fingers and listen to echoes. Can you hear the shape of the room? Intuitively, and for simple room shapes, we know that this is possible. A shoebox room, for example, has well-defined modes, from which we can derive its size. However, the question is challenging in more general cases, even if we presume that the room impulse response (RIR) contains an arbitrarily long set of echoes (assuming an ideal, noiseless measurement) that should specify the room geometry.It might appear that Kac's problem and the question we pose are equivalent. This is not the case, for the sound of a drum depends on more than its set of resonant frequencies (eigenvalues)-it also depends on its resonant modes (eigenvectors). In the paper "Drums that sound the same" (5), Chapman explains how to construct drums of different shapes with matching resonant frequencies. Still, these drums would hardly sound the same if hit with a drumstick. They share the resonant frequencies, but the impulse responses are different. Even a single drum struck at ...

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Section: Practical Algorithmmentioning

confidence: 99%

“…There have been several attempts in estimating the room geometry from RIRs (12)(13)(14). In (13), the problem is formulated in 2D, and the authors take advantage of multiple source locations to estimate the geometry.…”

mentioning

confidence: 99%

Acoustic echoes reveal room shape

Dokmanić

Parhizkar

Walther

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

224

230

View full text Add to dashboard Cite

show abstract

“…First one is acoustic room geometry reconstruction: in a series of works [1][2][3] it has been shown that we can reconstruct polyhedral rooms from RIRs recorded by a few microphones, and we can do it even with a single source [1].…”

Section: Introductionmentioning

confidence: 99%

From acoustic room reconstruction to slam

Dokmanić

Daudet

Vetterli

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recent works on reconstruction of room geometry from echoes assume that the geometry of the sensor array is known. In this paper, we show that such an assumption is not essential; echoes provide sufficient clues to reconstruct the room's and the array's geometries jointly, even from a single acoustic event. Rather than focusing on the combinatorial problem of matching the walls and the recorded echoes, we provide algorithms for solving the joint estimation problem in practical cases when this matching is known and the number of microphones is small. We then explore intriguing connections between this problem and simultaneous localization and mapping (SLAM), and show that SLAM can be solved by the same methods. Finally, we demonstrate how effective the proposed methods are by numerical simulations and experiments with real measured room impulse responses.

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“…Localizing the direct sound is a well understood problem [15]. In ad-hoc deployment, recent works propose a calibration step to locate the main reflectors [14,16,17,18]. Note that there is in fact no necessity to know the room ge ometry exactly, the positions of the image sources being suf ficient.…”

Section: Introductionmentioning

confidence: 99%

Raking echoes in the time domain

Scheibler

Vetterli

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The geometry of room acoustics is such that the reverberant signal can be seen as the same waveform emitted from multiple locations. In analogy with the rake receiver from wireless com munications, we propose several beamforming strategies that exploit, rather than suppress, this additional spatio-temporal di versity. Unlike earlier work in the frequency domain, time do main designs allow to shape the impulse response of the beam former. In particular, we can control perceptually relevant pa rameters, such as the amount of early echoes or the length of the beamformer response.Relying on the knowledge of the image sources positions, we derive different optimal beamformers. Leveraging percep tual cues, we show how to improve interference and noise re duction without degrading the perceptual quality. The designs are validated through simulation. Using early echoes is shown to strictly improve the signal to interference and noise ratio. Code and speech samples are available online at http: / / lcav.epfl.ch/Robin_Scheibler.

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Geometrically Constrained Room Modeling With Compact Microphone Arrays

Cited by 49 publications

References 16 publications

Acoustic echoes reveal room shape

Acoustic echoes reveal room shape

From acoustic room reconstruction to slam

Raking echoes in the time domain

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