Abstract
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.
Highlights
In the past two decades, microphone array technology has consistently been a hot research field.Microphone arrays are mainly used for sound source localization and identification, and have been an important practical technology with many valuable applications, such as noise source localization [1,2], target sound source tracking [3], teleconferencing systems [4,5], intelligent robots [6,7,8], and so on.In microphone array technology, there are three main methods for sound source localization, namely, beamforming, acoustic holography, and time difference of arrival (TDOA)
The method proposed is a numerical approach based on the particle swarm optimization (PSO) algorithm, which can optimize the array structure of an arbitrary number of microphones under any specific localization scenarios without prior array structure information
The bigger the input noise, the more significant the gap. This means that the optimized arrays by the proposed method could improve the accuracy and robustness of the sound source localization based on TDOA
Summary
In the past two decades, microphone array technology has consistently been a hot research field. There are three main methods for sound source localization, namely, beamforming, acoustic holography, and time difference of arrival (TDOA). It can be seen that a great deal of research work has been done in the field of microphone array optimization for sound source localization These array optimization methods are mainly based on the beamforming and acoustic holography methods. Hu et al [40] proposed an analytical method based on TDOA to optimize microphone array structure, which could guarantee that the sound source localization had the same performance in all directions for omni-directional estimation. The method proposed is a numerical approach based on the particle swarm optimization (PSO) algorithm, which can optimize the array structure of an arbitrary number of microphones under any specific localization scenarios without prior array structure information.
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