Maximum likelihood estimation of direction of arrival using an acoustic vector-sensor

Levin, Dovid Y.; Habets, Emanuël A. P.; Gannot, Sharon

doi:10.1121/1.3676699

Cited by 64 publications

(28 citation statements)

References 32 publications

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“…[1][2][3] Accuracy of source localization and array gain are improved with increasing number of array elements. Using acoustic vector sensors which combine sound pressure and particle velocity shows promise; [4][5][6] however, these sensors can be expensive. New techniques for improving array performance with fewer conventional microphone elements are the subject of much contemporary research.…”

Section: [Mrb]mentioning

confidence: 99%

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation

Bush

Xiang

2015

The Journal of the Acoustical Society of America

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Coprime arrays represent a form of sparse sensing which can achieve narrow beams using relatively few elements, exceeding the spatial Nyquist sampling limit. The purpose of this paper is to expand on and experimentally validate coprime array theory in an acoustic implementation. Two nested sparse uniform linear subarrays with coprime number of elements ( M and N) each produce grating lobes that overlap with one another completely in just one direction. When the subarray outputs are combined it is possible to retain the shared beam while mostly canceling the other superfluous grating lobes. In this way a small number of microphones ( N+M-1) creates a narrow beam at higher frequencies, comparable to a densely populated uniform linear array of MN microphones. In this work beampatterns are simulated for a range of single frequencies, as well as bands of frequencies. Narrowband experimental beampatterns are shown to correspond with simulated results even at frequencies other than the arrays design frequency. Narrowband side lobe locations are shown to correspond to the theoretical values. Side lobes in the directional pattern are mitigated by increasing bandwidth of analyzed signals. Direction of arrival estimation is also implemented for two simultaneous noise sources in a free field condition.

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Section: [Mrb]mentioning

confidence: 99%

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation

Bush

Xiang

2015

The Journal of the Acoustical Society of America

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“…An array of AVHs is introduced for multiple-source localization problems in [5]. A maximum-likelihood algorithm was developed in [6]. Conventional beamforming (Bartlett beamforming) and Capon beamforming (minimum-variance distortionless response beamforming) for twodimensional direction of arrival (DOA) estimation using an array of AVHs were investigated in [7].…”

Section: Introductionmentioning

confidence: 99%

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

2018

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Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

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“…Though significant progress has been made, DOA estimation under noisy and reverberant environment is still a challenging task. To improve DOA performance, maximum steered response power (MSRP) [8] and maximum likelihood (ML) [9] methods were introduced, and the effects of noise and reverberation on these DOA estimators have been analyzed.…”

Section: Introductionmentioning

confidence: 99%

Joint Noise and Reverberation Adaptive Learning for Robust Speaker DOA Estimation with an Acoustic Vector Sensor

Wang¹,

Zou²

2018

Interspeech 2018

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Deep neural network (DNN) based DOA estimation (DNN-DOAest) methods report superior performance but the degradation is observed under stronger additive noise and room reverberation conditions. Motivated by our previous work with an acoustic vector sensor (AVS) and the great success of DNN based speech denoising and dereverberation (DNN-SDD), a unified DNN framework for robust DOA estimation task is thoroughly investigated in this paper. First, a novel DOA cue termed as sub-band inter-sensor data ratio (Sb-ISDR) is proposed to efficiently represent DOA information for training a DNN-DOAest model. Second, a speech-aware DNN-SDD is presented, where coherence vectors denoting the probability of time-frequency points dominated by speech signals are used as additional input to facilitate the training to predict complex ideal ratio masks. Last, by stacking the DNN-DOAest on the DNN-SDD with a joint part, the unified network is jointly finetuned, which enables DNN-SDD to serve as a pre-processing front-end to adaptively generate 'clean' speech features that are easier to be correctly classified by the following DNN-DOAest for robust DOA estimation. Experimental results on simulated and recorded data confirm the effectiveness and superiority of our proposed methods under different noise and reverberations compared with baseline methods.

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Maximum likelihood estimation of direction of arrival using an acoustic vector-sensor

Cited by 64 publications

References 32 publications

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

Joint Noise and Reverberation Adaptive Learning for Robust Speaker DOA Estimation with an Acoustic Vector Sensor

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