Eigen Beam Based Sound Source Localization Algorithms Evaluation on a Non-Spherical Microphone Array

2019 ◽  
Author(s):  
Shan Gao ◽  
Rui Liu ◽  
Xihong Wu ◽  
Tianshu Qu
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Localization Algorithms

scholarly journals DATASET FOR EVALUATION OF THE PERFORMANCE OF THE METHODS OF SOUND SOURCE LOCALIZATION ALGORITHMS USING TETRAHEDRAL MICROPHONE ARRAYS

2020 ◽  
Vol 12 (0) ◽  
pp. 1-8
Author(s):  
Saulius Sakavičius
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Direction Of Arrival ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Direction Of Arrival Estimation ◽  
Sound Sources ◽  
Localization Algorithms ◽  
Geometrical Information

For the development and evaluation of a sound source localization and separation methods, a concise audio dataset with complete geometrical information about the room, the positions of the sound sources, and the array of microphones is needed. Computer simulation of such audio and geometrical data often relies on simplifications and are sufficiently accurate only for a specific set of conditions. It is generally desired to evaluate algorithms on real-world data. For a three-dimensional sound source localization or direction of arrival estimation, a non-coplanar microphone array is needed.Simplest and most general type of non-coplanar array is a tetrahedral array. There is a lack of openly accessible realworld audio datasets obtained using such arrays. We present an audio dataset for the evaluation of sound source localization algorithms, which involve tetrahedral microphone arrays. The dataset is complete with the geometrical information of the room, the positions of the sound sources and the microphone array. Array audio data was captured for two tetrahedral microphone arrays with different distances between microphones and one or two active sound sources. The dataset is suitable for speech recognition and direction-of-arrival estimation, as the signals used for sound sources were speech signals.

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 Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4326 ◽  
Author(s):  
Haitao Liu ◽  
Thia Kirubarajan ◽  
Qian Xiao
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Optimization Model ◽  
Microphone Array ◽  
Optimization Method ◽  
Sound Source Localization ◽  
Positioning Accuracy ◽  
Specific Localization ◽  
Array Structures ◽  
Array Optimization

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

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