Estimation of reflective surfaces from continuous signals

Tervo, Sakari; Korhonen, Teemu

doi:10.1109/icassp.2010.5496104

Cited by 20 publications

(17 citation statements)

References 6 publications

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“…An 1 regularized least squares method is used in [3] to estimate the geometry of a shoebox room by measuring many single wall impulse responses under different angles. A different approach is proposed in [4] where the authors do not assume an a priori knowledge of the excitation signal, therefore not assuming the knowledge of the impulse response either. An approach based on acoustic imaging is proposed in [5].…”

Section: Prior Artmentioning

confidence: 99%

Can one hear the shape of a room: The 2-D polygonal case

Dokmanić

Vetterli

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the problem of estimating room geometry from the acoustic room impulse response (RIR). Existing approaches addressing this problem exploit the knowledge of multiple RIRs. In contrast, we are interested in reconstructing the room geometry from a single RIR-a 1-D function of time. We discuss the uniqueness of the mapping between the geometry of a planar polygonal room and a single RIR. In addition to this theoretical analysis, we also propose an algorithm that performs the "blindfolded" room estimation. Furthermore, the derived results are used to construct an algorithm for localization in a known room using only a single RIR. Verification of the theoretical developments with numerical simulations is given before concluding the paper.

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Section: Prior Artmentioning

confidence: 99%

Can one hear the shape of a room: The 2-D polygonal case

Dokmanić

Vetterli

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Spherical harmonics were employed in [10,11], to localize reflections. As in [12], they employed continuous signals, however, having issues with low SNRs. In [13], reflectors were localized by first estimating the related image source positions [14], having knowledge of the reflections' times of arrival (TOAs).…”

Section: Introductionmentioning

confidence: 99%

Acoustic Reflector Localization and Classification

Remaggi

Kim

Jackson

et al. 2018

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

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The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized. Recent studies in acoustic reflector localization usually assume the room to be free from furniture. Here, by analyzing the scattered reflections, an algorithm is also proposed to binary classify reflectors into room boundaries and interior furniture. Experiments were conducted in four rooms. The classification algorithm showed high quality performance, also improving the localization accuracy, for non-static listener scenarios.

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“…the problem of estimating the geometry of the environment through acoustic acquisitions is an area of in creasing interest. In [3] a method is proposed for estimating the reflectors based on the inverse mapping of the acoustic multi-path propagation problem. In [4] the parameters of a constrained room model are estimated through f\ -regularized least-squares.…”

Section: Introductionmentioning

confidence: 99%

Exact localization of acoustic reflectors from quadratic constraints

Canclini

Antonacci

Thomas

et al. 2011

2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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In this paper we discuss a method for localizing acoustic reflectors in space based on acoustic measurements on source-to-microphone reflective paths. The method converts Time of Arrival (TOA) and Time Difference of Arrival (TDOA) into quadratic constraints on the line corresponding to the reflector. In order to be robust against measurement errors we derive an exact solution for the min imization of a cost function that combines an arbitrary number of quadratic constraints. Moreover we propose a new method for the analytic prediction of reflector localization accuracy. This method is sufficiently general to be applicable to a wide range of estimation problems.Index Terms-Microphone arrays. space-time audio process ing. environment reconstruction. acoustic reflector localizationKnowing the geometry of the acoustic environment can be very use ful for numerous space-time processing applications. For example. in [1] source localization is approached using a maximum likeli hood estimator whose data model incorporates the prediction of the early reflections. In [2] an environment-aware acoustic rendering system is proposed. in which early reflections are included in the propagation model from the loudspeakers to the listening area. This has the result of making their rendering system robust to mild re verberation. Consequently. the problem of estimating the geometry of the environment through acoustic acquisitions is an area of in creasing interest. In [3] a method is proposed for estimating the reflectors based on the inverse mapping of the acoustic multi-path propagation problem. In [4] the parameters of a constrained room model are estimated through f\ -regularized least-squares. In [5] the problem of the estimation of the room geometry is approached through the measurement of the Times of Arrival (TOAs) of the re flective path from the source to the microphone. Here TOAs are converted into geometric constraints that locate the line that the re flector lies upon. For a single source-microphone pair such con straints express that this line should be tangential to an ellipse that is parameterized by the locations of the source and the microphone and by the TOA. Using multiple observations with a microphone array. the reflector is found as the common tangent to all such el lipses. which is estimated through the iterative minimization of a The cost functions defined in [5. 6] are inherently nonlinear. therefore they exhibit numerous local minima in which adaptive optimization algorithms could easily become trapped. particularly in the presence of relevant measurement errors. In this paper we propose an exact minimization procedure that determines the cor rect global minimum of the cost function while circumventing the problem of local minima. The problem is reformulated as the con strained minimization of a second-order polynomial. which admits an exact solution. This reformulation is inspired by [7]. where a source localization problem is approached with an exact minimiza tion of a constrained least-squares c...

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Estimation of reflective surfaces from continuous signals

Cited by 20 publications

References 6 publications

Can one hear the shape of a room: The 2-D polygonal case

Can one hear the shape of a room: The 2-D polygonal case

Acoustic Reflector Localization and Classification

Exact localization of acoustic reflectors from quadratic constraints

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