Guoli Ping

Huawei Technologies (France)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Zhigang Chu

Chongqing University

Yang Yang

Zhejiang Industry Polytechnic College

Zhongming Xu

Chongqing University

Liming Shi

Chongqing University of Posts and Telecommunications

Mads Græsbøll Christensen

Aalborg University

Linbang Shen

Merchants Chongqing Communications Research and Design Institute

Sankha Subhra Bhattacharjee

Aalborg University

Peter Gerstoft

Scripps Institution of Oceanography

Jesper Rindom Jensen

Aalborg University

Efrén Fernández-Grande

Technical University of Denmark

Cooperative Institutions

Chongqing University

Aalborg University

Chinese Academy of Sciences

Huawei Technologies (France)

Scripps Institution of Oceanography

University of California, San Diego

Institute of Acoustics

Nanjing University of Information Science and Technology

Chongqing University of Technology

International Audio Laboratories Erlangen

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Resolution enhancement of two-dimensional grid-free compressive beamforming

The Journal of the Acoustical Society of America (2018)

Yang Yang Zhigang Chu Guoli Ping Zhongming Xu

Reconstructing the acoustic source distribution via imposing a sparsity constraint on a continuum, the atomic norm minimization (ANM) based grid-free compressive beamforming can eliminate the basis mismatch of conventional grid-based compressive beamforming. However, it works well only for sufficiently separated sources, which prohibits high resolution. The drawback arises because it uses an atomic norm to measure the source sparsity, while the atomic norm is not a direct sparse metric and its minimization is equivalent to the sparsity constraint only when the sources are sufficiently separated. This paper devotes itself to overcoming the drawback for the two-dimensional ANM based grid-free compressive beamforming. First, a sparse metric that can promote sparsity to a greater extent than the atomic norm is proposed. Then, using this metric a minimization problem is formulated and the majorization−minimization (MM) solving algorithm is introduced. MM iteratively conducts atomic norm minimization with a sound reweighting strategy, and therefore the developed method can be termed as iterative reweighted atomic norm minimization (IRANM). Both simulations and experiments demonstrate that whether a standard uniform rectangular array or a non-uniform array constituted by a small number of microphones is utilized, IRANM can overcome the drawback and thus enhance the resolution.

Minification

10.1121/1.5042239

Cite

Citations (23)

Sparse planar arrays for azimuth and elevation using experimental data

The Journal of the Acoustical Society of America (2021)

Santosh Nannuru Peter Gerstoft Guoli Ping Efrén Fernández-Grande

Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.

Planar array

Direction of arrival

Sparse array

Anechoic chamber

Array Processing

Elevation (ballistics)

10.1121/10.0002988

Cite

Citations (12)

Three-dimensional source localization using sparse Bayesian learning on a spherical microphone array

The Journal of the Acoustical Society of America (2020)

Guoli Ping Efrén Fernández-Grande Peter Gerstoft Zhigang Chu

The identification of acoustic sources in a three-dimensional (3D) domain based on measurements with an array of microphones is a challenging problem: it entails the estimation of the angular position of the sources (direction of arrival), distance relative to the array (range), and the quantification of the source amplitudes. A 3D source localization model using a rigid spherical microphone array with spherical wave propagation is proposed. In this study, sparse Bayesian learning is used to perform localization in 3D space and examine the use of principal component analysis to denoise the measurement data. The performance of the proposed method is examined numerically and experimentally, which is tested both in a free-field and in a reverberant environment. The numerical and experimental investigations demonstrate that the approach offers accurate localization in a 3D domain, resolving closely spaced sources and making it possible to identify sources located at different ranges.

Position (finance)

Sparse array

10.1121/10.0001383

Cite

Citations (52)

A refined wideband acoustical holography based on equivalent source method

Scientific Reports (2017)

Guoli Ping Zhigang Chu Zhongming Xu Linbang Shen

Abstract This paper is concerned with acoustical engineering and mathematical physics problem for the near-field acoustical holography based on equivalent source method (ESM-based NAH). An important mathematical physics problem in ESM-based NAH is to solve the equivalent source strength, which has multiple solving algorithms, such as Tikhonov regularization ESM (TRESM), iterative weighted ESM (IWESM) and steepest descent iteration ESM (SDIESM). To explore a new solving algorithm which can achieve better reconstruction performance in wide frequency band, a refined wideband acoustical holography (RWAH) is proposed. RWAH adopts IWESM below a transition frequency and switches to SDIESM above that transition frequency, and the principal components of input data in RWAH have been truncated. Further, the superiority of RWAH is verified by the comparison of comprehensive performance of TRESM, IWESM, SDIESM and RWAH. Finally, the experiments are conducted, confirming that RWAH can achieve better reconstruction performance in wide frequency band.

Wideband

Frequency band

Regularization

10.1038/srep43458

Cite

Citations (12)

Generation of Personal Sound Fields in Reverberant Environments Using Interframe Correlation

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Liming Shi Guoli Ping Xiaoxiang Shen Mads Græsbøll Christensen

Personal sound field control techniques aim to produce sound fields for different sound contents in different places of an acoustic space without interference. The limitations of the state-of-the-art methods for sound field control include high latency and computational complexity, especially in the cases when the reverberation time is long and number of loudspeakers is large. In this paper, we propose a personal sound field control approach that exploits interframe correlation. Considering the past frames, the proposed method can accommodate long reverberation time with a low latency. To find the optimal parameters for the physical meaningful constraints, the subspace decomposition and Newton's method are applied. Furthermore, a sound field distortion oriented subspace construction method is proposed to reduce the subspace dimension. Compared with traditional methods, simulation results show that the proposed algorithm is able to obtain a good trade-off between acoustic contrast and reproduction error with a low latency for measured room impulse responses.

Architectural acoustics

10.1109/icassp43922.2022.9747574

Cite

Citations (1)

Fast Fourier-based deconvolution for three-dimensional acoustic source identification with solid spherical arrays

Mechanical Systems and Signal Processing (2018)

Yang Yang Zhigang Chu Linbang Shen Guoli Ping Zhongming Xu

10.1016/j.ymssp.2018.01.028

Cite

Citations (13)

Wideband holography based spherical equivalent source method with rigid spherical arrays

Mechanical Systems and Signal Processing (2018)

Guoli Ping Zhigang Chu Yang Yang Zhongming Xu

Acoustic Holography

Wideband

Regularization

Signal reconstruction

Identification

10.1016/j.ymssp.2018.04.006

Cite

Citations (16)

Iteratively Reweighted Spherical Equivalent Source Method for Acoustic Source Identification

IEEE Access (2019)

Guoli Ping Zhigang Chu Yang Yang Xu Chen

Spherical equivalent source method (S-ESM) using rigid spherical microphone arrays can simultaneously identify sound sources in all directions. In this paper, based on the reweighting and sparse representation frameworks, the sparsity-promoting iteratively reweighted least squares (IRLS) and reweighted ℓ ₁ -norm minimization (referred as w-ℓ ₁ -norm) are exploited to improve the performance of acoustic source identification for S-ESM. The numerical and experimental results indicate accurate acoustic source identification for the two iteratively reweighted algorithms. IRLS can provide good acoustic source identification over the wide frequency and measurement distance ranges, improving the performance of the established S-ESM. In addition, w-ℓ ₁ -norm is also an alternative solution strategy for S-ESM, although at the expense of low computational efficiency and given prior information.

Identification

Minification

10.1109/access.2019.2911857

Cite

Citations (7)

Compressive Spherical Beamforming for Acoustic Source Identification

Acta acustica united with Acustica (2019)

Guoli Ping Zhigang Chu Yang Yang

This study examines a compressive spherical beamforming (CSB) method, using a rigid spherical microphone array to localize and quantify the acoustic contribution of sources. The method relies on the array signal model in the spherical harmonics domain that can be represented as a spatially sparse problem. This makes it possible to use compressive sensing to solve an underdetermined problem via promoting sparsity. The estimation of the angular position of sources with respect to the microphone array, as well as the three-dimensional localization over a volume are investigated. Several sparse recovery algorithms [orthogonal matching pursuit (OMP), generalized OMP, &rhov ; 1 -norm minimization, and reweighted &rhov ; 1 -norm minimization] are examined for this purpose. The numerical and experimental results indicate that sparse recovery methods outperform conventional spherical harmonics beamforming. Reweighted &rhov ; 1 -norm has good adaptability to noise, improving the robustness of CSB. The method can successfully localize the angular position of sources, and quantify their relative pressure contribution. The method is promising to localize sources in a three-dimensional domain of interest. However, the three-dimensional localization is more sensitive to noise, source distance, and properties of the sensing matrix than the two-dimensional localization.

Underdetermined system

Robustness

10.3813/aaa.919406

Cite

Citations (10)

Low Complexity Signal Adaptive Sound Zone Control Using Subspace Tracking

Sankha Subhra Bhattacharjee Andreas Jonas Fuglsig Jesper Rindom Jensen Liming Shi Guoli Ping

Signal subspace

SIGNAL (programming language)

Tracking (education)

10.1109/iwaenc61483.2024.10694133

Cite

Citations (0)