Shallow water sound source localization using the iterative beamforming method in an image framework

2017 ◽  
Vol 395 ◽  
pp. 354-370 ◽  
Author(s):  
Xun Wang ◽  
Shahram Khazaie ◽  
Luca Margheri ◽  
Pierre Sagaut
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization

Sound-Source Localization in Range-Dependent Shallow-Water Environments Using a Four-Layer Model

2019 ◽  
Vol 44 (1) ◽  
pp. 220-228 ◽  
Author(s):  
Xun Wang ◽  
Shahram Khazaie ◽  
Dimitri Komatitsch ◽  
Pierre Sagaut
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization ◽  
Layer Model

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

scholarly journals Multimodal Fusion for Indoor Sound Source Localization

2021 ◽  
pp. 107906
Author(s):  
Jinhui Chen ◽  
Ryoichi Takashima ◽  
Xingchen Guo ◽  
Zhihong Zhang ◽  
Xuexin Xu ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Multimodal Fusion ◽  
Sound Source Localization
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