Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

Liang, Longkai; Shi, Yiran; Shi, Yaowu; Bai, Zhejia; He, Wenchao; Lv, Xuhao

doi:10.1177/09574565221128066

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…For future work, DOA estimation can be applied not only to aeroacoustic direction finding using a microphone but also to scenarios with a large quantity of non-Gaussian mixed noise (such as impulse noise interference) in practical applications (e.g., underwater DOA estimation) and sparse signals collected in small snapshot scenarios. Therefore, the study of real-time estimation of sparse DOA in non-Gaussian white noise scenarios [45] is one of the main directions we aim to explore in the future. Additionally, we only designed the ESRNN-L/ESRNN-G structure for noise filtering of DOA estimation in this work, but sound event location is only a subbranch of sound event detection and location [46].…”

Section: Mic Formatmentioning

confidence: 99%

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

Zhang,

He,

Liu

et al. 2024

Preprint

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Sound event location is a critical aspect of two-dimensional direction-of-arrival (2D-DOA) estimation, predicting azimuth and elevation angles in 3D Cartesian coordinates for active sound events using multi-label regression. Challenges with conventional methods like the multi-signal classification (MUSIC) algorithm and baseline convolution recurrent neural network (BCRNN) include decreased precision and high computational demands, particularly in low signal-to-noise ratio (SNR) environments (SNR\textless-5 dB). Our work introduces an innovative solution, the effective residual self-attention recurrent neural network (ESRNN). ESRNN addresses distortion problems in low SNR conditions caused by the MUSIC algorithm, also enhancing 2D-DOA prediction accuracy in various SNR-reverberation scenarios. We propose two filter structures, ESRNN-L and ESRNN-G, tailored for SNRs above 0 dB and below -5 dB, respectively. Evaluating on TAU Spatial Sound Events 2019 datasets with synthetic SNRs from -10 dB to 30 dB, our experiments demonstrate ESRNN-L achieves a 21 $%$ lower 2D-DOA error than BCRNN at SNRs below -5 dB. Additionally, ESRNN-G exhibits a 15$%$ lower error with a 10$%$ parameter reduction when SNRs exceed 0 dB. When compared with other principal attention methods through ablation study, it also showcases the model's efficiency and robustness.

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Section: Mic Formatmentioning

confidence: 99%

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

Zhang,

He,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For feature work, DOA estimation can not only be applied to aeroacoustic direction finding using microphone but also has a great quantity of non-Gaussian mixed noise (such as impulse noise interference) in many practical application scenarios (such as underwater DOA estimation) and sparse signal collected in small snapshot scenarios. Therefore, the study of real-time estimation of sparse DOA estimation in non-Gaussian white noise scenarios [36] is one of the main directions we want to explore in the future. In addition, we only design ESRNN-L/ESRNN-G structure for noise filtering of DOA estimation in this work but sound event location is only a subbranch of sound event detection and location [37].…”

Section: ) Data Preparation Processingmentioning

confidence: 99%

ESRNN: Effective Residual Self-Attention Recurrent Neural Network for Sound Event Location

Zhang¹

2023

Preprint

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<p>Sound event location is a momentous subtask of two-dimensional direction-of-arrival (2D-DOA) estimation to forecast the azimuth and elevation angle from each active sound event class of an audio fragment using multi-label regression in the 3D Cartesian coordinates. The main problem with the traditional multi-signal classification (MUSIC) algorithm and the existing baseline convolution recurrent neural network (BCRNN) are lower precision and huge computation in weaker signal-noise-ratio (SNR) environment. In particular, MUSIC algorithm will cause complete distortion when the SNR is lower than -5 dB. We thus design effective residual self-attention recurrent neural network (ESRNN) not only to overcome the distortion of traditional MUSIC algorithm under lower SNR but also further reduce predicted 2D-DOA error in different SNR reverberation environments. Two different filter structures, ESRNN-L and ESRNN-G, are designed to improve the SNR of our model when the SNR is above 0 dB or below -5 dB in a targeted manner respectively. To verify the efficiency and robustness, we train and test our model using TAU Spatial Sound Events 2019 datasets extracted the phase and magnitude spectrogram with different synthetic SNRs ranging from -10 dB to 30 dB. The experimental results show that the optimal 2D-DOA error of our ESRNN-L is 21% lower than BCRNN when the SNR is less than -5 dB, and ESRNN-G is 15% error lower with 10% parameters reduction when the SNR is higher than 0 dB.</p>

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“…However, as indicated above, they either face the problem of time consumption and off-grid. To solve the problems of off-grid and impulsive noise in sparse signal processing, by using the PFLOM to suppress impulsive noise, a three-step alternating iterative orthogonal matching pursuit algorithm is proposed in [44] to achieve the off-grid DOA estimation for an AVSA.…”

Section: Introductionmentioning

confidence: 99%

Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

Liu,

Zhang,

Wang

et al. 2023

JMSE

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Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector sensor array (AVSA). Firstly, to remove high outliers from the array output data, the output information of the AVSA is weighted by using the infinite norm. To further suppress outliers, a p-order cost function is formulated by extending the Frobenius norm to lower order, and then the expression of the signal power is quantified. Lastly, the DOA is approximated on the signal power by a spectral peak search mechanism. DOA estimation results based on Monte Carlo simulations validate the accuracy and robustness of the proposed techniques herein compared to the current, available methods in the context of intense impulsive noise, low generalized signal–to–noise ratio (GSNR), and a smaller number of snapshots.

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Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

Cited by 4 publications

References 31 publications

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

ESRNN: Effective Residual Self-Attention Recurrent Neural Network for Sound Event Location

Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

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