Research and Improvement of Beamforming Algorithm for Low Signal-to-Noise Ratio and Low Sampling Points

Zhang, Han; Cheng, Lei; Li, Yang

doi:10.1109/aicit55386.2022.9930151

Cited by 1 publication

(1 citation statement)

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“…The traditional MUSIC algorithm loses resolution completely when the SNR is lower than -5 dB [44]. Therefore, our focus is on comparing the test results of different models when the SNR is higher than 0 dB, as presented in Table 4.…”

Section: Experimental Results Analysismentioning

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: Experimental Results Analysismentioning

confidence: 99%