Noise source identification with Beamforming in the pass-by of a car

Ballesteros, José A.; Sarradj, Ennes; Fernández, Marcos D.; Geyer, Thomas; Ballesteros, Ma Jesús

doi:10.1016/j.apacoust.2015.01.019

Cited by 44 publications

(22 citation statements)

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“…Microphone arrays in combination with spatial filtering methods have found widespread usage in recent years. Typical scenarios where beamforming is used include industrial and environmental noise source identification as well as automotive and aeroacoustic applications [16] , [17] , [18] , [19] , [20] . In most of these cases, a manual evaluation of the results in terms of source maps per third-octave band is usually necessary, hence requiring an expert to make sense of the data.…”

Section: Introductionmentioning

confidence: 99%

Acoustic source localization with microphone arrays for remote noise monitoring in an Intensive Care Unit

et al. 2018

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An approach is described to apply spatial filtering with microphone arrays to localize acoustic sources in an Intensive Care Unit (ICU). This is done to obtain more detailed information about disturbing noise sources in the ICU with the ultimate goal of facilitating the reduction of the overall background noise level, which could potentially improve the patients’ experience and reduce the time needed for recovery. This paper gives a practical description of the system, including the audio hardware setup as well as the design choices for the microphone arrays. Additionally, the necessary signal processing steps required to produce meaningful data are explained, focusing on a novel clustering approach that enables an automatic evaluation of the spatial filtering results. This approach allows the data to be presented to the nursing staff in a way that enables them to act on the results produced by the system.

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Section: Introductionmentioning

confidence: 99%

Acoustic source localization with microphone arrays for remote noise monitoring in an Intensive Care Unit

et al. 2018

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“…Future studies will be designed to generalize the initial method to all types of movement, and particularly for higher speeds, for which the Doppler effect has to be taken into account [27], [7], [28]. The deconvolution method should be modified accordingly.…”

Section: Conclusion and Further Studiesmentioning

confidence: 99%

Localization and Contribution of Underwater Acoustical Sources of a Moving Surface Ship

Oudompheng¹,

Nicolas²,

Lamotte³

2018

IEEE J. Oceanic Eng.

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“…The acoustic imaging techniques have played a central role in the Noise, Vibration, and Harshness (NVH) engineering field of sound source identification, such as automotive, aircraft, and industrial applications [ 1 , 2 , 3 ]. As one of the excellent acoustic imaging tools, near-field acoustical holography (NAH) was first formalized four decades ago by the groundbreaking work of Williams [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Ratio Model of L1/L2 Norm for Sound Source Identification

Huang

Zhang

et al. 2020

Sensors

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In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.

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Noise source identification with Beamforming in the pass-by of a car

Cited by 44 publications

References 10 publications

Acoustic source localization with microphone arrays for remote noise monitoring in an Intensive Care Unit

Acoustic source localization with microphone arrays for remote noise monitoring in an Intensive Care Unit

Localization and Contribution of Underwater Acoustical Sources of a Moving Surface Ship

A Ratio Model of L1/L2 Norm for Sound Source Identification

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