Time-difference of arrival model for spherical microphone arrays and application to direction of arrival estimation

Nikunen, Joonas; Virtanen, Tuomas

doi:10.23919/eusipco.2017.8081409

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…The time delay of a microphone at location m i and a reference point at the origin 0 = [0, 0, 0] T is given by [5]:…”

Section: A Semblance Based Tdoa Algorithmmentioning

confidence: 99%

“…For the sound source localization problem, one of the main techniques employed is based on the time difference of arrival (TDOA), i.e., the delay that the propagating wave (sound) arrives at several microphones disposed in different locations [2]- [5]. However, in applications involving Unmanned Aerial Vehicle (UAV) the main problem arises from the ego noise and the fact that the sound source location and the microphones can be in movement [1].…”

Section: Introductionmentioning

confidence: 99%

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A semblance based TDOA algorithm for sound source localization

Aldeia¹,

Crispim²,

Barreto³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this paper we propose a new time difference delay of arrival technique based on the semblance multichannel coherency function for the problem of sound source localization. The proposed algorithm was tested on recordings from an Unmanned Aerial Vehicle (UAV) equipped with an array of 8 microphones, for estimating the azimuth and elevation angles of a speech based source. Our results shown that the semblance method has proven to have a good performance, obtaining good results regardless of the ego noise even in cases where the signalto-noise ratio (SNR) was very low.

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“…The time delay of a microphone at location m i and a reference point at the origin 0 = [0, 0, 0] T is given by [5]:…”

Section: A Semblance Based Tdoa Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A semblance based TDOA algorithm for sound source localization

Aldeia¹,

Crispim²,

Barreto³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

show abstract

“…2 , the corresponding delay for the microphone located at m k ∈ R 3 , using as a reference point 0 = [0, 0, 0] T [6], is calculated by:…”

Section: Semblance Based Tdoa Algorithmmentioning

confidence: 99%

On the Semblance Based TDOA Algorithm for Sound Source Localization: a parametric study

Aldeia¹,

Ferreira²,

Filho³

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Recently proposed, the Semblance Based TDOA Algorithm for Sound Source Localization uses a cross-correlation function to determine the direction of a sound source. This article aims to perform a parametric analysis of the algorithm, applying a hypothesis test to determine the importance of each parameter. The results show that, regarding the three original parameters of the algorithm, one presented small sensitivity and can be discarded, and the remaining can be defined based on the desired resolution level, or fixed at low values (at cost of a slower runtime).

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“…2-dimensional array structures include square [20], cross [21], spiral [22,23], and circular geometries [24]. 3-dimensional arrays mainly include cubic [25], pyramidal [25], hemispherical [26], and spherical [27] geometries. Relevant scholars have analyzed the performance of various arrays, indicating that each kind of array structure is only suitable for specific localization algorithms and scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Kirubarajan

Xiao

2019

Sensors

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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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Time-difference of arrival model for spherical microphone arrays and application to direction of arrival estimation

Cited by 5 publications

References 13 publications

A semblance based TDOA algorithm for sound source localization

A semblance based TDOA algorithm for sound source localization

On the Semblance Based TDOA Algorithm for Sound Source Localization: a parametric study

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

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