Improving the efficiency of deconvolution algorithms for sound source localization

Lylloff, Oliver; Fernández-Grande, Efrén; Agerkvist, Finn T.; Hald, Jørgen; Roig, Elisabet Tiana; Andersen, Martin S.

doi:10.1121/1.4922516

Cited by 56 publications

(19 citation statements)

References 25 publications

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“…The computational grid of DAMAS needs to be fine enough, and too thick grid points will also cause spatial mixing of sound source distribution. In addition, DAMAS has the disadvantages of large computation and long operation time 16 . Some studies have shown that DAMAS with low frequency resolution has some advantages for the larger aperture array 17 .…”

Section: Deconvolution Algorithmsmentioning

confidence: 99%

Parameter Investigation of Ring Microphone Array for Rotating Sound Source Localization

Zhou¹,

Bao²,

Yang³

et al. 2019

Proceedings of Global Power &Amp; Propulsion Society

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As society developed expeditiously, more attentions have been paid towards harm of noise. Hence, noise reduction has become a significant in industries. Localization of rotating sound source plays a main role in the process of noise reduction. Ring microphone array is essential in order to localize the rotating sound source using frequency domain algorithm, since it's able to construct a virtual array rotating synchronously with the rotating sound source in order to remove Doppler Effect. Array radius and the number of microphones play their key roles in ring microphone array， which they will significantly affect the measurement accuracy and costs. In spite of that, there is no evidence proof that radius and microphone number have an impact on the accuracy of rotating sound source localization. The intention for this study is to qualitatively analyze the impacts of radius and microphone number on the measurement accuracy. The intensity, position, resolution and dynamic range of the initial sound source are regarded as evaluation indexes by using rotational beamforming, deconvolution algorithms to compare the results. As conclusion, only when the ratio of the array radius to the rotating radius of the sound source is nearly 1 and the ratio of microphone number to the circumference of the ring array within 128/0.5π~32/0.5π, rotating beamforming would have a better spatial resolution and dynamic range. Moreover, the intensity error obtained by deconvolution algorithm is only 0.0128dB, and the position deviation has no business with array parameters. In a nutshell, this study provided guidance in localization of rotating sound source using ring microphone array, where it kept a balance between measurement accuracy and cost needed.

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Section: Deconvolution Algorithmsmentioning

confidence: 99%

Parameter Investigation of Ring Microphone Array for Rotating Sound Source Localization

Zhou¹,

Bao²,

Yang³

et al. 2019

Proceedings of Global Power &Amp; Propulsion Society

View full text Add to dashboard Cite

show abstract

“…After conversion to the PSF shift‐invariant model, fast algorithms based on the fast Fourier transform (FFT) can be used to solve the problem (e.g. DAMAS2 [14], FISTA–DAMAS [22], FFT–NNLS [23], FFT–RL [24]). Unfortunately, in the underwater environment, it is difficult to establish a suitable mapping relationship to convert a PSF shift‐variant model into a PSF shift‐invariant model.…”

Section: Introductionmentioning

confidence: 99%

Improved underwater acoustic imaging with non‐uniform spatial resampling RL deconvolution

Mei

Pei

Zakharov

et al. 2020

IET Radar, Sonar & Navigation

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Underwater acoustic imaging (UAI) can be utilized to observe the spatial distribution of a near-field sound source. The image quality depends on the resolution and sidelobe level of conventional beamforming. The linear array based UAI can be considered as deconvolution of a two-dimensional point spread function shift-variant model. The performance of UAI can be improved via innovative deconvolution algorithms. In this study, a non-uniform spatial resampling Richardson-Lucy (RL) fast algorithm is designed in which the amount of samples is determined by the power of the UAI output. This allows for a significant decrease in the number of samples compared to the traditional RL algorithm with similar positioning accuracy. Computer simulations and sea trials are performed to validate the effectiveness and feasibility of the proposed method.

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“…In addition, research on sound detection technology for the design of microphone arrays and sound source localization algorithms [15][16][17][18][19] has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

Xing

Yang

2019

Applied Sciences

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To reduce the negative effect on sound source localization when the source is at an extreme angle and improve localization precision and stability, a theoretical model of a three-plane five-element microphone array is established, using time-delay values to judge the sound source’s quadrant position. Corresponding judgment criteria were proposed, solving the problem in which a single-plane array easily blurs the measured position. Based on sound source geometric localization, a formula for the sound source azimuth calculation of a single-plane five-element microphone array was derived. The sinusoids and cosines of two elevation angles based on two single-plane arrays were introduced into the sound source spherical coordinates as composite weighted coefficients, and a sound source localization fusion algorithm based on a three-plane five-element microphone array was proposed. The relationship between the time-delay estimation error, elevation angle, horizontal angle, and microphone array localization performance was discussed, and the precision and stability of ranging and direction finding were analyzed. The results show that the measurement precision of the distance from the sound source to the array center and the horizontal angle are improved one to threefold, and the measurement precision of the elevation angle is improved one to twofold. Although there is a small error, the overall performance of the sound source localization is stable, reflecting the advantages of the fusion algorithm.

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Improving the efficiency of deconvolution algorithms for sound source localization

Cited by 56 publications

References 25 publications

Parameter Investigation of Ring Microphone Array for Rotating Sound Source Localization

Parameter Investigation of Ring Microphone Array for Rotating Sound Source Localization

Improved underwater acoustic imaging with non‐uniform spatial resampling RL deconvolution

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

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