Acoustic Source Localization in Aircraft Interiors Using Microphone Array Technologies

Sklanka, Bernard J.; Tuss, Joel R.; Buehrle, Ralph D.; Klos, Jacob; Williams, Earl G.; Valdivia, Nicolas

doi:10.2514/6.2006-2714

Cited by 3 publications

(1 citation statement)

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“…It is an inverse problem, since the goal is to infer locations given the measurements of the sound field. This problem arises in different fields, such as speech and sound enhancement [21], [28], speech recognition [29], acoustic tomography [4], robotics [1] and aeroacoustics [30].…”

Section: A First Example: Regularization With the Wave Equationmentioning

confidence: 99%

Physics-Driven Inverse Problems Made Tractable With Cosparse Regularization

Kitić

Albera

Bertin

et al. 2016

IEEE Trans. Signal Process.

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Sparse data models are powerful tools for solving illposed inverse problems. We present a regularization framework based on the sparse synthesis and sparse analysis models for problems governed by linear partial differential equations. Although nominally equivalent, we show that the two models differ substantially from a computational perspective: unlike the sparse synthesis model, its analysis counterpart has much better scaling capabilities and can indeed be faster when more measurement data is available. Our findings are illustrated on two examples, sound source localization and brain source localization, which also serve as showcases for the regularization framework. To address this type of inverse problems, we develop a specially tailored convex optimization algorithm based on the Alternating Direction Method of Multipliers.

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Section: A First Example: Regularization With the Wave Equationmentioning

confidence: 99%