Microphone configuration for beamformer design using the Taguchi method

Chan, Kit Yan; Yiu, Ka Fai Cedric; Nordholm, Sven

doi:10.1016/j.measurement.2016.10.025

Cited by 7 publications

(6 citation statements)

References 45 publications

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“…In [ 20 ], an approach based on compressive sensing is described for building wideband sparse microphone arrays. The Taguchi method makes an effort to perform systematic experiments based upon orthogonal arrays to analyze the microphone design after pre-selecting multiple possible positions for the microphone elements [ 21 ]. By making the filter length sufficiently long, a nonlinear optimization problem on filter coefficients and microphone array placement using the

norm is presented in [ 8 ].…”

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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norm is presented in [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Zhang,

Li,

Yiu

2024

Sensors

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“…This technique is called array thinning and has been successfully implemented in antenna arrays [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, there are only a few papers devoted to thinning microphone arrays [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Gao et al [ 18 ] considered a problem of sparse beamformer design in the case of the norm

and, based on the properties of this norm, they were able to reduce the size of the microphone array. In [ 16 ], a thinning of a microphone array was proposed based on the Taguchi method. The authors studied the effectiveness of the Taguchi method in determining the microphone configuration.…”

Section: Introductionmentioning

confidence: 99%

A General Scheme of a Branch-and-Bound Approach for the Sensor Selection Problem in Near-Field Broadband Beamforming

Wielgus,

Szlachetko

2024

Sensors

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This paper is devoted to the sensor selection problem. A broadband receiver beamforming working in a near-field is considered. The system response should be as close as possible to the desired one, which is optimized in the sense of L2 norm. The problem considered is at least NP-hard. Therefore, the branch-and-bound algorithm is developed to solve the problem. The proposed approach is universal and can be applied not only to microphone arrays but also to antenna arrays; that is, the methodology for the generation of consecutive solutions can be applied to different types of sensor selection problems. Next, for a larger microphone array, an efficient metaheuristic algorithm is constructed. The algorithm implemented is a hybrid genetic algorithm based on the ITÖ process. Numerical experiments show that the proposed approach can be successfully applied to the sensor selection problem.

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“…A classical and typical application scenario is when speech signals are recorded by pre-mounted microphones in the wireless acoustic sensor network (WASN). Two common popular methods to perform speech extraction are blind source separation (BSS) and beamforming [2,3,4,5]. The latter is very sensitive to array model mismatch [6,7,3] and the former requires less assumptions of the acoustic scene or system.…”

Section: Introductionmentioning

confidence: 99%

“…Two common popular methods to perform speech extraction are blind source separation (BSS) and beamforming [2,3,4,5]. The latter is very sensitive to array model mismatch [6,7,3] and the former requires less assumptions of the acoustic scene or system.…”

Section: Introductionmentioning

confidence: 99%

Sensor placement optimization of blind source separation in a wireless acoustic sensor network via hybrid descent methods

Wang

Low

et al. 2022

Applied Acoustics

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Microphone configuration for beamformer design using the Taguchi method

Cited by 7 publications

References 45 publications

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method

A General Scheme of a Branch-and-Bound Approach for the Sensor Selection Problem in Near-Field Broadband Beamforming

Sensor placement optimization of blind source separation in a wireless acoustic sensor network via hybrid descent methods

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