Position calibration of microphones and loudspeakers in distributed computing platforms

Raykar, Vikas C.; Kozintsev, I.; Lienhart, Rainer

doi:10.1109/tsa.2004.838540

Cited by 126 publications

(102 citation statements)

References 14 publications

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“…In addition, most experiments have been simulations involving narrowband or non-speech signals. The conditions in [7] are more in line with the problem addressed in this article: an adhoc network of microphones with no prior knowledge on their relative locations. The approach in that case, however, benefits from a specific device platform in which each microphone is co-located with a loudspeaker from which a known calibration signal can be emitted.…”

Section: Introductionsupporting

confidence: 55%

“…To allow microphone array beamforming algorithms to be applied in this case, microphone array shape calibration must first be performed to automatically determine the microphone positions. Existing work towards array shape calibration has significant limiting constraints, such as the need for synchronised playback of known calibration signals and a good initial estimate of microphone locations [6], [7], [8], [9], [10]. In addition, most experiments have been simulations involving narrowband or non-speech signals.…”

Section: Introductionmentioning

confidence: 99%

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Microphone Array Shape Calibration in Diffuse Noise Fields

McCowan

Lincoln

Himawan

2008

IEEE Trans. Audio Speech Lang. Process.

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Section: Introductionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Microphone Array Shape Calibration in Diffuse Noise Fields

McCowan

Lincoln

Himawan

2008

IEEE Trans. Audio Speech Lang. Process.

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“…, 8}) is the position of the th vertex of cuboid V . Note that (17) implies that comparisons are the only operations required to perform the coherence test, since the TDOF bounds for each cuboid can be precomputed just once and stored in a lookup table.…”

Section: Region-based Searchmentioning

confidence: 99%

“…In such scenarios, synchronization problems are not a major concern, since all sources and sinks are usually under the control of whoever wants to calibrate the microphone array. On the other hand, if the sources' positions are unknown, then a joint source and sensor localization is commonly employed [17,18]. In this case, the lack of synchronism may severely degrade the mobile position estimate.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Sensor Self-Localization: Models and Recent Results

Haddad

Lima

Martins

et al. 2017

Wireless Communications and Mobile Computing

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The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.

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“…One technique used for sensor network calibration is to manually measure the inter-distance between pairs of microphones and use multi-dimensional scaling to compute microphone locations, (Birchfield and Subramanya, 2005). Another option is to use GPS, (Niculescu and Nath, 2001), or to use additional transmittors (radio or audio), close to each receiver, (Elnahrawy et al, 2004;Raykar et al, 2005;Sallai et al, 2004). Sensor network calibration is treated in (Biswas and Thrun, 2004).…”

Section: Introductionmentioning

confidence: 99%

Understanding Toa and Tdoa Network Calibration Using Far Field Approximation as Initial Estimate

Kuang

Ask

Burgess

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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Abstract:This paper presents a study of the so called far field approximation to the problem of determining both the direction to a number of transmittors and the relative motion of a single antenna using relative distance measurements. The same problem is present in calibration of microphone and wifi-transmittor arrays. In the far field approximation we assume that the relative motion of the antenna is small in comparison to the distances to the base stations. The problem can be solved uniquely with at least three motions of the antenna and at least six real or virtual transmittors. The failure modes of the problem is determined to be (i) when the antenna motion is planar or (ii) when the transmittor directions lie on a cone. We also study to what extent the solution can be obtained in these degenerate configurations. The solution algorithm for the minimal case can be extended to the overdetermined case in a straightforward manner. We also implement and test algorithms for non-linear optimization of the residuals. In experiments we explore how sensitive the calibration is with respect to different degrees of far field approximations of the transmittors and with respect to noise in the data.

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Position calibration of microphones and loudspeakers in distributed computing platforms

Cited by 126 publications

References 14 publications

Microphone Array Shape Calibration in Diffuse Noise Fields

Microphone Array Shape Calibration in Diffuse Noise Fields

Acoustic Sensor Self-Localization: Models and Recent Results

Understanding Toa and Tdoa Network Calibration Using Far Field Approximation as Initial Estimate

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