Physical-model based efficient data representation for many-channel microphone array

Koyano, Yuji; Yatabe, Kohei; Ikeda, Yusuke; Oikawa, Yasuhiro

doi:10.1109/icassp.2016.7471699

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…In addition to conclusions from antenna theory (Williams, 1999), it is experimentally well-known that increasing both the aperture (Sachar et al, 2001) and the number of microphones (Weinstein et al, 2007) improve the global performances of localization. Thanks to the outcome of microphones based on the technology called Microelectromechanical Systems (MEMS), developing a large microphone array with several hundreds of microphones becomes easier and low-cost compared with older technologies (Hafizovic et al, 2012;Koyano et al, 2016;Vanwynsberghe et al, 2015). At the same time, original multichannel processing methods anticipate and rely on the use of a great number of sensors: three examples are dereverberation (Chardon et al, 2015), source separation (FitzGerald et al, 2016) and monitoring (Lai et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Geometric calibration of very large microphone arrays in mismatched free field

Vanwynsberghe

Challande

Ollivier

et al. 2019

The Journal of the Acoustical Society of America

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Large microphone arrays are an efficient means for source localization thanks to a wide aperture and a great number of sensors. When such arrays are deployed in situ, accurate geometric calibration becomes essential to obtain the microphone positions. In free field, the classic procedures rely on measured Times Of Arrival (TOA) or Time Differences of Arrival (TDOA) between the microphones and several controlled sources. However free field model mismatches, such as reflectors, generate outliers which severely deteriorate the positioning accuracy. This paper introduces a unified framework for robust calibration using TOA or TDOA, by exploiting an outlier-aware noise model. Thanks to the largeness of the array, the existing outliers are sparse and can be identified by a Lasso regression. From this, three iterative robust solvers a are proposed: (i) for TOA by Robust Multi Dimensional Unfolding, a particular variation of Robust Multi Dimensional Scaling (ii) for TDOA by data predenoising based on sparse and low-rank matrix decomposition, and (iii) for TDOA by jointly identifying the outliers and the geometry. The relevance of outlier-aware approaches is asserted by numerical and experimental tests. Compared with the baseline least-square approaches, the proposed robust solvers significantly improve the positioning accuracy in a free field mismatched by reflectors.

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Section: Introductionmentioning

confidence: 99%

Geometric calibration of very large microphone arrays in mismatched free field

Vanwynsberghe

Challande

Ollivier

et al. 2019

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…Our solution focuses on the structured sparsity of the plane wave representation for the reconstruction of parameters of the Room Transfer Function (RTF). In literature, sparse plane wave representation has been used not only for the representation of the wavefield in a room in low frequency domain, but also for efficient storage of highly correlated recordings of dense microphone arrays [8]. 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].…”

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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“…5 The sparsity in the image-source model has been mainly used for the estimation of the room shape 6 and the estimation of the direction of arrivals of early echoes, 7 the sparsity of plane wave representation of the sound pressure is used for the characterization of the sound fields inside the room. 8 The sparsity of the room modes may also be exploited in the low-frequency range of the room transfer functions (RTF). 9 By RTF we denote the ration between the received and emitted signal in Fourier domain.…”

Section: Introductionmentioning

confidence: 99%

Localization of sound sources in a room with one microphone

Tukuljac

Lissek

2017

Wavelets and Sparsity XVII

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Estimation of the location of sound sources is usually done using microphone arrays. Such settings provide an environment where we know the difference between the received signals among different microphones in the terms of phase or attenuation, which enables localization of the sound sources. In our solution we exploit the properties of the room transfer function in order to localize a sound source inside a room with only one microphone. The shape of the room and the position of the microphone are assumed to be known. The design guidelines and limitations of the sensing matrix are given. Implementation is based on the sparsity in the terms of voxels in a room that are occupied by a source. What is especially interesting about our solution is that we provide localization of the sound sources not only in the horizontal plane, but in the terms of the 3D coordinates inside the room.

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Physical-model based efficient data representation for many-channel microphone array

Cited by 10 publications

References 18 publications

Geometric calibration of very large microphone arrays in mismatched free field

Geometric calibration of very large microphone arrays in mismatched free field

Joint Estimation of the Room Geometry and Modes with Compressed Sensing

Localization of sound sources in a room with one microphone

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