Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms

Plinge, Axel; Jacob, Florian; Haeb‐Umbach, Reinhold; Fink, Gernot A.

doi:10.1109/msp.2016.2555198

Cited by 77 publications

(39 citation statements)

References 36 publications

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“…The tracking accuracy is highly dependent on the specific algorithm used for localization. Moreover, tracking approaches that rely on TDoA estimates are crucially dependent on accurate calibration [142] and synchronization [143]. To relax the dependency on calibration and synchronization, DoA estimates can be used as observations instead of TDoA estimates.…”

Section: Tracking Of Moving Sourcesmentioning

confidence: 99%

The LOCATA Challenge: Acoustic Source Localization and Tracking

Evers

Löllmann

Mellmann

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

128

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The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates. The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This article provides a review of relevant localization and tracking algorithms and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.

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Section: Tracking Of Moving Sourcesmentioning

confidence: 99%

The LOCATA Challenge: Acoustic Source Localization and Tracking

Evers

Löllmann

Mellmann

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

128

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“…An overall review of geometric calibration was done in (Plinge et al, 2016), and a more specific one on T(D)OA approaches by Wang et al (Wang et al, 2016, tab. 1).…”

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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“…An important task when a group of people record the same event with their devices is to enhance the input signals and to localize sound sources [6], [7]. In order to employ traditional microphone array techniques with ad-hoc networks, specific challenges such as device localization [2], [3] and clock synchronization [4], [5] have to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Determined Blind Source Separation for Ad-hoc Microphone Networks

Wang

Cavallaro

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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We propose a pseudo-determined blind source separation framework that exploits the information from a large number of microphones in an ad-hoc network to extract and enhance sound sources in a reverberant scenario. After compensating for the time offsets and sampling rate mismatch between (asynchronous) signals, we interpret as a determined M × M mixture the over-determined M × N mixture, where M > N is the number of microphones and N is the number of sources. Next, we propose a pseudo-determined mixture model that can apply an M ×M independent component analysis (ICA) directly to the M-channel recordings. Moreover, we propose a reference-based permutation alignment scheme that aligns the permutation of the ICA outputs and classifies them into target channels, which contain the N sources, and non-target channels, which contain reverberation residuals. Finally, using the signals from non-target channels, we estimate in each target channel the power spectral density of the noise component that we suppress with a spectral post-filter. Interestingly, we also obtain latereverberation suppression as by-product. Experiments show that each processing block improves incrementally source separation and that the performance of the proposed pseudo-determined separation improves as the number of microphones increases.

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Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms

Cited by 77 publications

References 36 publications

The LOCATA Challenge: Acoustic Source Localization and Tracking

The LOCATA Challenge: Acoustic Source Localization and Tracking

Geometric calibration of very large microphone arrays in mismatched free field

Pseudo-Determined Blind Source Separation for Ad-hoc Microphone Networks

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