A constrained total least squares calibration method for distributed microphone array

Wang, Rong; Chen, Zhe; Yin, Fuliang

doi:10.1016/j.apacoust.2018.05.022

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Self-localization based only on TDoA measurements usually exploits low-rank properties. Examples of methods that employ TDoA measurements include a three-step stratification process with rank constraints and factorization [ 15 ], a low-rank approximation using a pseudo-matrix of Time of Arrival (ToA)s [ 16 ], and a constrained total least squares approach [ 17 ]. Unfortunately, all of the aforementioned passive approaches are derived either for a specific number of sensors and sources, or the convergence of the method to the optimal solution is often troublesome.…”

Section: Introductionmentioning

confidence: 99%

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

Woźniak,

Kowalczyk

2023

Sensors

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The network of distributed microphone arrays is usually established in an ad hoc manner; hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution; furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.

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Section: Introductionmentioning

confidence: 99%

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

Woźniak,

Kowalczyk

2023

Sensors

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“…The experimental results showed that the fitting error of the spherical radius grew in an exponential ratio acccompanied by a reduction of the segment angle [16]. As a variant of NLSF, the constrained nonlinear least squares fitting (CNLSF) algorithm has been used in various areas, such as the metallurgical industry, microphone position calibration, circle fitting, digital holography, GPS compass attitude determination, etc [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A new high-precision sphere-fitting method with small segment angles

Fei

et al. 2020

Meas. Sci. Technol.

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Measuring a spherical surface with small segment angle (e.g. 1°) precisely is a big challenge in scientific research and engineering applications. To fit sphere surfaces with small segment angles with high precision, a five-parameter constrained nonlinear least square fitting (CNLSF) method is presented in this work, where an Adam optimization algorithm with a prominent anti-interference ability is employed. We discuss various factors which influence the final uncertainty evaluation of the sphere’s radius. A Monte Carlo simulation is conducted by superimposing Gaussian noise on the ideal spherical data to simulate practical instrument errors, then the relationship between the fitting bias of the sphere’s radius and noise the level, the number of sampling points and the segment angle is investigated by comparing the performance of the linear least square fitting algorithm, the nonlinear least square fitting algorithm and the CNLSF algorithm. Finally, the simulated and experimental results show that the CNLSF method performs more precisely and robustly than the other algorithms, even when the corresponding segment angle approaches 1°, which illustrates that it is a promising and practical method in the real measurement world.

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A distributed particle filter with sampling-based consensus density fusion for speaker tracking in distributed microphone networks

Jing,

Yao,

2024

Applied Acoustics

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A constrained total least squares calibration method for distributed microphone array

Cited by 6 publications

References 32 publications

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

A new high-precision sphere-fitting method with small segment angles

A distributed particle filter with sampling-based consensus density fusion for speaker tracking in distributed microphone networks

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