Optimal microphone placement for indoor acoustic localization using evolutionary optimization

Macho-Pedroso, Roberto; Domingo-Perez, Francisco; Velasco, Jose; Losada, Cristina; Macías-Guarasa, Javier

doi:10.1109/ipin.2016.7743609

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…Several studies have employed heuristic methods to identify the global optimal solution since the problem of optimization is non-convex and nonlinear. These include evolutionary programming [ 12 ], genetic algorithm (GA) [ 13 , 14 , 15 , 16 ], simulated annealing algorithm [ 17 ], pattern-search algorithm [ 18 ], and differential evolution [ 19 ]. However, these methods are often very time-consuming and have the risk of missing the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Zhang,

Li,

Yiu

2024

Sensors

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In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results.

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Section: Introductionmentioning

confidence: 99%

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Zhang,

Li,

Yiu

2024

Sensors

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“…The optimal static positioning of microphones in sensor networks has been addressed over a few decades [11], [22], [23], however, the tracking of moving sources through reconfigurable and/or moving microphone networks has been addressed only recently. In [24], an evolutionary strategy for the optimal static placement of microphones in an indoor search region is proposed. It is based on a spatial-likelihood function built on a spatial map of the acoustic power for a given sensor network configuration.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Target Tracking Through a Cluster of Mobile Agents

Drioli

Giordano

Salvati

et al. 2021

IEEE Trans. Cybern.

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This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and

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“…The global positioning system requires to select the optimal subset of the visible satellites for minimizing the geometric dilution of precision [1], [2]. This is the sensor selection problem selecting the optimum (in terms of the quality of acquired data) p sensors from n-potential sensors locations, and such kind of sensor selection problem can be seen in various types of measurements such as acoustic measurements [3], [4], structural health monitoring [5], [6], and environment monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM

Nagata,

Nonomura,

Nakai

et al. 2020

Preprint

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The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm. The performance of the proposed method was evaluated with a random sensor problem and compared with the previously proposed methods such as the greedy method and the convex relaxation. The performance of the proposed method is better than an existing method in terms of the A-optimality criterion. In addition, the proposed method requires longer computational time than the greedy method but it is quite shorter than the convex relaxation in large-scale problems. The proposed method was applied to the data-driven sparse-sensor-selection problem. A data set adopted is the NOAA OISST V2 mean sea surface temperature set. At the number of sensors larger than that of the latent state variables, the proposed method showed similar and better performances compared with previously proposed methods in terms of the A-optimality criterion and reconstruction error.

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Optimal microphone placement for indoor acoustic localization using evolutionary optimization

Cited by 11 publications

References 25 publications

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Acoustic Target Tracking Through a Cluster of Mobile Agents

Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM

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