Multi-Level Time-Frequency Bins Selection for Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

Geng, Jie; Wang, Sifan; Lou, Xin; Liu, Qinglai

doi:10.1109/taslp.2022.3155276

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…No extra augmented Lagrangian is needed here for solving (31). That is to say, the steps between line 17 and line 24 in Algorithm 1 are replaced by the following two-step scheme for estimating α (l+1) :…”

Section: Scenariomentioning

confidence: 99%

“…Compared with the planar and spherical microphone arrays, acoustic vector sensors (AVS) for source localization [26][30] require less space and computation, as the bins are selected by comparing the phases of the real and imaginary parts of the microphone signals without eigenvalue decomposition [28]. Further enhancement have been achieved through outlier removal [31], modelling outliers as an extra Gaussian cluster [32], generelizing single source dominance TF bin to single source dominance window [33], and deconvolution of the global pseudo-spectrum by iteratively source detection [34] for the sources with small separations. All these TF bin selection based methods require empirically chosen threshold values for different experiment configurations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Xiong,

Bao,

Zhou

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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Section: Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Xiong,

Bao,

Zhou

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…However, various disturbances in real scenarios bring challenges to DOA estimation. [7]. As recording conditions become more complex (e.g., higher reverberation times, more sound sources, and smaller angle separation), there is a large research space for multisource localization to obtain precise DOA estimates.Early methods estimated the time difference of arrival (TDOA) [8,9], which is calculated by generalised crosscorrelation (GCC).…”

mentioning

confidence: 99%

Multisource localization based on angle distribution of time–frequency points using an FOA microphone

Tao

Jia

et al. 2023

CAAI Trans on Intel Tech

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Multisource localization occupies an important position in the field of acoustic signal processing and is widely applied in scenarios, such as human‐machine interaction and spatial acoustic parameter acquisition. The direction‐of‐arrival (DOA) of a sound source is convenient to render spatial sound in the audio metaverse. A multisource localization method in a reverberation environment is proposed based on the angle distribution of time–frequency (TF) points using a first‐order ambisonics (FOA) microphone. The method is implemented in three steps. 1) By exploring the angle distribution of TF points, a single‐source zone (SSZ) detection method is proposed by using a standard deviation‐based measure, which reveals the degree of convergence of TF point angles in a zone. 2) To reduce the effect of outliers on localization, an outlier removal method is designed to remove the TF points whose angles are far from the real DOAs, where the median angle of each detected zone is adopted to construct the outlier set. 3) DOA estimates of multiple sources are obtained by postprocessing of the angle histogram. Experimental results in both the simulated and real scenarios verify the effectiveness of the proposed method in a reverberation environment, which also show that the proposed method outperforms reference methods.

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First-Order Relative Harmonic Coefficient-Based Time-Frequency Points Selection for Multi-Source DOA Estimation

Tao,

Jia,

Bao

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Multi-Level Time-Frequency Bins Selection for Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

Cited by 3 publications

References 43 publications

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Multisource localization based on angle distribution of time–frequency points using an FOA microphone

First-Order Relative Harmonic Coefficient-Based Time-Frequency Points Selection for Multi-Source DOA Estimation

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