A hierarchical approach to unsupervised shape calibration of microphone array networks

Hennecke, Marius H.; Plötz, Thomas; Fink, Gernot A.; Schmalenstroer, Jorg; Häb‐Umbach, Reinhold

doi:10.1109/ssp.2009.5278589

Cited by 24 publications

(12 citation statements)

References 7 publications

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“…Prior work focused on the calibration of a distributed synchronously sampled microphone array [6]. The employed high-quality audio equipment allowed for a calibration using only ambient noise and naturally occurring sounds.…”

Section: Related Workmentioning

confidence: 99%

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Towards acoustic self-localization of ad hoc smartphone arrays

Hennecke¹,

Fink²

2011

2011 Joint Workshop on Hands-Free Speech Communication and Microphone Arrays

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The advent of the smartphone in recent years opened new possibilities for the concept of ubiquitous computing. We propose to use multiple smartphones spontaneously assembled into an ad hoc microphone array as part of a teleconferencing system. The unknown spatial positions, the asynchronous sampling and the unknown time offsets between clocks of smartphones in the ad hoc array are the main problems for such an application as well as for almost all other acoustic signal processing algorithms. A maximum likelihood approach using time of arrival measurements of short calibration pulses is proposed to solve this self-localization problem. The global orientation of each phone, obtained by the means of nowadays common built-in geomagnetic compasses, in combination with the constant microphone-loudspeaker distance lead to a nonlinear optimization problem with a reduced dimensionality in contrast to former methods. The applicability of the proposed self-localization is shown in simulation and via recordings in a typical reverberant and noisy conference room.

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Section: Related Workmentioning

confidence: 99%

“…A matching procedure robust against localization outliers leads to the global spatial array configuration. Unlike [6] we focus in this contribution on the calibration of low-quality unsynchronized mobile phone audio hardware and resort to acoustic calibration signals to approach the self-localization task.…”

Section: Related Workmentioning

confidence: 99%

Towards acoustic self-localization of ad hoc smartphone arrays

Hennecke¹,

Fink²

2011

2011 Joint Workshop on Hands-Free Speech Communication and Microphone Arrays

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“…This is due to the fact that TDOA-based localization algorithms do not accurately estimate the range of a source when its distance is much greater than the array baseline. Indeed, the algorithms presented in [4], [5] recognize this fact, and perform calibration based on DOA measurements only.…”

Section: B Proposed Solutionmentioning

confidence: 99%

“…In [4] and [5] the authors have presented methods based on estimated Directions of Arrival (DOAs). In [4], DOAs are obtained from the analysis of acoustic maps generated using a delay and sum beamformer.…”

Section: Introductionmentioning

confidence: 99%

Geometric calibration of distributed microphone arrays from acoustic source correspondences

Valente

Tagliasacchi

Antonacci

et al. 2010

2010 IEEE International Workshop on Multimedia Signal Processing

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This paper proposes a method that solves the problem\ud of geometric calibration of microphone arrays. We consider a\ud distributed system, in which each array is controlled by separate\ud acquisition devices that do not share a common synchronization\ud clock. Given a set of probing sources, e.g. loudspeakers, each\ud array computes an estimate of the source locations using a\ud conventional TDOA-based algorithm. These observations are\ud fused together by the proposed method, in order to estimate\ud the position and pose of one array with respect to the other.\ud Unlike previous approaches, we explicitly consider the anisotropic\ud distribution of localization errors. As such, the proposed method\ud is able to address the problem of geometric calibration when the\ud probing sources are located both in the near- and far-field of\ud the microphone arrays. Experimental results demonstrate that\ud the improvement in terms of calibration accuracy with respect\ud to state-of-the-art algorithms can be substantial, especially in the\ud far-fiel

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“…In [2,3] a single array provides near-field 3D source locations which are used to solve the relative rotation and translation of two arrays. In far-field, only directional estimates of sources can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Closed-form self-localization of asynchronous microphone arrays

Pertilä

Mieskolainen

Hämäläinen

2011

2011 Joint Workshop on Hands-Free Speech Communication and Microphone Arrays

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The utilization of distributed microphone arrays in many speech processing applications such as beamforming and speaker localization rely on the precise knowledge of microphone locations. Several selflocalization approaches have been presented in the literature but still a simple, accurate, and robust method for asynchronous devices is lacking. This work presents an analytical solution for estimating the positions and rotations of asynchronous loudspeaker equipped microphone arrays or devices. The method is based on emitting and receiving calibration signals from each device, and extracting the time of arrival (TOA) values. Utilizing the knowledge of array geometry in the TOA estimation is proposed to improve accuracy of translation. Results with measurements using four devices on a table surface demonstrates a mean translation error of 11 mm with standard deviation of 6 mm and mean z-axis rotation error of 0.11 (rad) with a standard deviation of 0.14 (rad) in contrast to computer vision annotations with 200 rotations and translation estimates.

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A hierarchical approach to unsupervised shape calibration of microphone array networks

Cited by 24 publications

References 7 publications

Towards acoustic self-localization of ad hoc smartphone arrays

Towards acoustic self-localization of ad hoc smartphone arrays

Geometric calibration of distributed microphone arrays from acoustic source correspondences

Closed-form self-localization of asynchronous microphone arrays

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