1,807 research outputs found
Active control of sound inside a sphere via control of the acoustic pressure at the boundary surface
Here we investigate the practical feasibility of performing soundfield
reproduction throughout a three-dimensional area by controlling the acoustic
pressure measured at the boundary surface of the volume in question. The main
aim is to obtain quantitative data showing what performances a practical
implementation of this strategy is likely to yield. In particular, the
influence of two main limitations is studied, namely the spatial aliasing and
the resonance problems occurring at the eigenfrequencies associated with the
internal Dirichlet problem. The strategy studied is first approached by
performing numerical simulations, and then in experiments involving active
noise cancellation inside a sphere in an anechoic environment. The results show
that noise can be efficiently cancelled everywhere inside the sphere in a wide
frequency range, in the case of both pure tones and broadband noise, including
cases where the wavelength is similar to the diameter of the sphere. Excellent
agreement was observed between the results of the simulations and the
measurements. This method can be expected to yield similar performances when it
is used to reproduce soundfields.Comment: 28 pages de text
A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping
The method described here performs blind deconvolution of the beamforming
output in the frequency domain. To provide accurate blind deconvolution,
sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term.
As the mean of the noise in the power spectrum domain is dependent on its
variance in the time domain, the proposed method includes a variance estimation
step, which allows more robust blind deconvolution. Validation of the method on
both simulated and real data, and of its performance, are compared with two
well-known methods from the literature: the deconvolution approach for the
mapping of acoustic sources, and sound density modeling
Efficient inversion methods in underwater acoustics
This dissertation describes efficient methods developed and implemented for source localization and sound speed and bottom depth estimation using sound propagation in the ocean. The proposed inversion techniques are based on the linearization of the generally non-linear inverse problem of parameter estimation in underwater acoustics. These techniques take into account properties of the ocean environment and are accurate in their estimation results without being prohibitively computationally intensive. For the inversion, select ray paths are taken into account: the direct, first surface bounce, and first bottom bounce. Ray travel time derivatives with respect to parameters that affect path arrival times are obtained analytically. These derivatives and a first order expansion are then used to find estimates of unknown parameters through replica and true paths; replica paths are generated using ray theory for underwater sound propagation and true paths are identified from measured time series. The linearization scheme works efficiently for the estimation of geometric parameters such as the source and receiver location coordinates and the depth of the water column. It is also successful in estimating the sound speed profile in the ocean using empirical orthogonal functions. In this work, the linearization inversion technique is applied to marine mammal tracking, and it is also used with real data collected during the Haro Strait experiment for source and receiver localization. For the Haro Strait data, inversion using linearization is also compared to matched-field processing, which estimates source location and geoacoustic parameters through a full field matching approach
์์ถ์ผ์ฑ ๊ธฐ๋ฐ ์ ๊ทผ๋ฒ์ ์ด์ฉํ ์์ค์ํฅ ์์์์ ์์น ์ถ์ ๊ธฐ๋ฒ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์กฐ์ ํด์๊ณตํ๊ณผ, 2021. 2. ์ฑ์ฐ์ .์ผ์ฐจ์ ์ํฅ ์์์์ ์์น์ถ์ ์ ์ ์์ฒด, ์ฐ๋์ฒด, ์บ๋นํ
์ด์
์์์์ ๋ถ์์ ์ํด ํ์์ ์ธ ๊ณผ์ ์ด๋ค. ์ ํต์ ์ธ ๋นํ์ฑ ๊ธฐ๋ฒ์ ๊ฐ์ธํ ์์น ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ ๊ณตํ๋, ํ๋์ ์์์์ ์์น๋ง์ ๊ตฌ๋ถํ ์ ์๋ ์ ํด์๋์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋ค. ๊ณ ํด์๋์ ์์น ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ป๊ธฐ ์ํด ์ต๊ทผ ์์ถ์ผ์ฑ ๊ธฐ๋ฐ์ ์์น ์ถ์ ๊ธฐ๋ฒ๋ค์ด ์ฌ์ฉ๋์ด ์ง๊ณ ์๋ค. ์์ถ์ผ์ฑ ๊ธฐ๋ฒ์ ํฌ์์ฑ์ ๊ฐ์ง ์ ํธ์ ํ๋,์ฒ๋ฆฌ,๋ณต์์ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ์ด๋ฉฐ ์์์ฒ๋ฆฌ, ์์ค์ํฅ, ์ต์ ํ ๋ฌธ์ ๋ฑ์์ ๋๋ฆฌ ํ์ฉ๋์ด์ง๊ณ ์๋ค. ์์ค ์์์์ ์์น ์ถ์ ์ ์ํ์ฌ ์์ถ์ผ์ฑ ๊ธฐ๋ฐฅ์ ๊ธฐ๋ฒ๋ค์ด ์ ์ฉ๋์ด ์์ผ๋ฉฐ ์ ํต์ ์ธ ๋นํ์ฑ ๊ธฐ๋ฒ์ ๋นํ์ฌ ํด์๋ ์ธก๋ฉด์์ ๋ ๋์ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค.
ํ์ง๋ง ์ด๋ฌํ ํด์๋ ์ธก๋ฉด์ ์ฑ๋ฅ ํฅ์์๋ ๋ถ๊ตฌํ๊ณ ์์ถ์ผ์ฑ ๊ธฐ๋ฐ์ ๋ฐฉ๋ฒ์ ์ฌ์ ํ ๋ฌธ์ ์ ๋ค์ ๊ฐ์ง๊ณ ์๋ค. ์ฒซ๋ฒ์งธ, ์์ถ์ผ์ฑ ๊ธฐ๋ฒ์ ์ ํต์ ์ธ ๋นํ์ฑ ๊ธฐ๋ฒ์ ๋นํด ์์น ์ฐ์ฐ ๊ณผ์ ์ด ๋ถ์์ ์ฑ์ ๊ฐ์ง๋ค. ๋น๋ก ๊ณ ํด์๋์ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ๋ ์์ถ์ผ์ฑ ๊ธฐ๋ฒ์ ์์นํด์ ๊ณผ์ ์์ ๋ถ์์ ํ ๋ชจ์ต์ ๋ณด์ฌ์ฃผ๋ฉฐ, ์์ ์ ์ธ ๋ณต์์ ์ ํดํ๋ค. ๋๋ฒ์งธ, ๊ธฐ์ ๋ถ์ผ์น๋ก ์ธํ ์ค์ฐจ๊ฐ ์ ํํ ์์์์ ์์น ์ถ์ ์ ์ ํดํ๋ค. ๊ฒ๋ค๊ฐ 3์ฐจ์ ์์์์ ์์น ์ถ์ ๋ฌธ์ ๋ ์ด๋ฌํ ๊ธฐ์ ๋ถ์ผ์น๋ฅผ ํด๊ฒฐํ ์ ์๋ ๊ธฐ๋ฒ์ด ์์ง๊น์ง ๊ฐ๋ฐ๋์ง ๋ชปํ์๋ค.
๋ณธ ๋
ผ๋ฌธ์ ๊ธฐ์กด์ ์์ถ์ผ์ฑ ๊ธฐ๋ฐ์ ์์น ์ถ์ ๊ธฐ๋ฒ์ด ๊ฐ์ง๋ ๋ฌธ์ ์ ์ ํ์
ํ๊ณ 3์ฐจ์ ์์น ์ถ์ ๋ฌธ์ ๋ฅผ ๋ค๋ฃฐ ์ ์๋ ํฅ์๋ ์์ถ์ผ์ฑ ๊ธฐ๋ฒ์ ์๊ฐํ๋ค. ํ์ ๊ณต๊ฐ ์ฌ์ด์ ๋์ ์๊ด๊ด๊ณ๋ก ์ธํ์ฌ ๋ฐ์ํ๋ ํด์ ๋ถ์์ ์ฑ์ ํด๊ฒฐ์๊ธฐ ์ํ์ฌ ``๋ค์ค์ฃผํ์ ์๊ด ์ฒ๋ฆฌ๊ธฐ๋ฒ"์ ์๊ฐํ๊ณ , 3์ฐจ์ ์์น ์ถ์ ๋ฌธ์ ์์ ๊ธฐ์ ๋ถ์ผ์น ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ ์ ์๋ ``์ ๋ ํ์ ๊ฒฉ์ ๊ธฐ๋ฒ"์ ์๊ฐํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ์ ํต์ ์ธ ๋นํ์ฑ ๊ธฐ๋ฒ์ ๋นํ์ฌ ์ ํํ ์์น ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ ๊ณตํ๋ฉฐ ์คํ ๋ฐ์ดํฐ๋ฅผ ํตํ ์์น ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ์ฌ ์ด๋ฌํ ์ฃผ์ฅ์ ๋ท๋ฐ์นจํ์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์์ค์ํฅ ์์์์ 3์ฐจ์ ์์น ์ถ์ ๋ฌธ์ ๋ฅผ ์ค์ ์ ์ผ๋ก ๋ค๋ฃจ์์ผ๋, ์ ์๋ ๊ธฐ๋ฒ์ ์๋ ๋ฐ ๋ ์ด๋, ์ํฅ ์์์ ์์น ์ถ์ ๋ฌธ์ ์๋ ํจ๊ณผ์ ์ผ๋ก ์ ์ฉ๋ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.Three-dimensional acoustic localization is an essential process to analyze the underwater sound sources such as submarine, scatterer, marine cavitation. Traditional beamforming processors provide robust localization results, however, the results show a low-resolution result which only reveals one dominant source location. In order to obtain the high resolution localization results, compressive sensing(CS) based approaches have been used recently. CS technique is an effective way for acquiring, processing, reconstructing the sparse signal and has wide applicability to many research fields such as image processing, underwater acoustics and optimization problems. For localizing the underwater acoustic sources, CS-based approaches have been adopted in many research fields and have shown better localization performance compared to the traditional beamforming processors in terms of resolution. Despite the performance improvement in resolution, there are still problems that need to be resolved when using the CS-based method. First, the CS-based method does not appear to be robust compared with the traditional beamforming processors. CS-based method provides high-resolution results, however, it suffers from computational instability which hinders the stable reconstruction. Second, basis mismatch error hindrances estimating the exact source locations. Moreover, there is no basis mismatch estimation technique applicable to 3D source localization problem.
This dissertation points out the limitation of conventional CS-based localization method and introduces the
advanced CS-based localization method which deals with 3D source localization problem. The ``coherent multiple-frequency processing" is introduced to overcome the instability of solution induced by high correlation of spatial grids and ``flexible searching-grid technique" is introduced to solve the basis mismatch problem which is developed for 3D source localization problem. The suggested techniques provide more accurate localization results compared to traditional beamforming processors or conventional CS-based beamforming processors and the arguments are backed with actual experimental data which was conducted in a cavitation tunnel. Though underwater acoustic source localization problems are presented in this dissertation, the proposed technique can be extended to many research fields, such as radar detection, sonar detection, ultrasound imaging.1 Introduction 2
1.1 Issue 1 : Computational Stability 4
1.2 Issue 2 : Basis Mismatch 5
1.3 Organization of the Dissertation 5
2 CS techniques for three-dimensional source localization 9
2.1 Compressive Sensing (CS) 9
2.2 Block-Sparse Compressive Sensing (BSCS) 11
2.3 Sparse Bayesian learning (SBL) 12
2.4 Off-Grid Sparse Bayesian Inference (OGSBI) 14
3 3D CS-based source localization method using multiple-frequency components 18
3.1 Introduction 18
3.2 Block-sparse Compressive Sensing for Incipient Tip Vortex Cavitation Localization 24
3.2.1 System framework for incipient tip vortex cavitation localization 24
3.2.2 Incoherent multiple-frequency localization with compressive sensing 26
3.2.3 Coherent multiple-frequency localization with block-sparse compressive sensing 28
3.3 Localization Results for Incipient TVC 32
3.3.1 Transducer source experiment 33
3.3.2 Incipient TVC Noise Source Experiment 36
3.4 Conclusion 41
3.5 Acknowledgments 43
4 3D CS-based source localization method by reducing the basis mismatch error 48
4.1 Introduction 48
4.2 Off grid system framework for 3D source localization 50
4.2.1 System framework for 3-dimensional off gird source localization 50
4.2.2 Coherent multiple-frequency localization with block-sparse Bayesian learning technique 53
4.2.3 3-dimensional off grid source localization method 55
4.3 Simulation and Experiment Results 62
4.4 Conclusion 65
5 Summary 70
Abstract (In Korean) 73Docto
Sparsity driven ultrasound imaging
An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function
This paper addresses the problem of speech separation and enhancement from
multichannel convolutive and noisy mixtures, \emph{assuming known mixing
filters}. We propose to perform the speech separation and enhancement task in
the short-time Fourier transform domain, using the convolutive transfer
function (CTF) approximation. Compared to time-domain filters, CTF has much
less taps, consequently it has less near-common zeros among channels and less
computational complexity. The work proposes three speech-source recovery
methods, namely: i) the multichannel inverse filtering method, i.e. the
multiple input/output inverse theorem (MINT), is exploited in the CTF domain,
and for the multi-source case, ii) a beamforming-like multichannel inverse
filtering method applying single source MINT and using power minimization,
which is suitable whenever the source CTFs are not all known, and iii) a
constrained Lasso method, where the sources are recovered by minimizing the
-norm to impose their spectral sparsity, with the constraint that the
-norm fitting cost, between the microphone signals and the mixing model
involving the unknown source signals, is less than a tolerance. The noise can
be reduced by setting a tolerance onto the noise power. Experiments under
various acoustic conditions are carried out to evaluate the three proposed
methods. The comparison between them as well as with the baseline methods is
presented.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language
Processin
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