Sound source feature defuzzification method based on end-to-end deep learning

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (21) : 133-141.

FENG Luoyi, ZAN Ming, XU Zhongming, ZHANG Zhifei, LI Zhenzhen

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Abstract

The grid-free sound source identification method using deep learning breaks through the limitation of grid division, and it has the advantages of high accuracy and fast prediction speed. When the Conventional Beamforming map (CB Map) is used to extract the sound source location features, the imaging performance of CB Map will be distorted with the decrease of the number of microphones, which will affect the accuracy of the deep learning model to predict the sound source location. To improve the generality of Deep Learning Grid-free Method (DL-GFM) and make it have good performance in different microphone array test conditions, an Array Converted Method (ACM) based on the end-to-end deep learning model U-Net to clear CB Map is proposed in this paper. Firstly, 18-channel array CB Map was used as the input and 64-channel array CB Map was used as the target to train U-Net model. Residual Network (ResNet) is trained as the model of DL-GFM method for source coordinate identification. The simulation results show that the ACM method has good ability of sidelobe elimination and main lobe width reduction, and it is also effective for the number of non-trained sound sources within the range of 1 to 8 sound sources. For the case of three sound sources, the ACM method improves the accuracy of DL-GFM method in the whole frequency band. Finally, the effectiveness and feasibility of the proposed method are verified by experiments with 1, 2 and 3 sound sources.

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Sound source identification / Beamforming / U-Net model / End-to-end deep learning / Phased microphone array

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FENG Luoyi, ZAN Ming, XU Zhongming, ZHANG Zhifei, LI Zhenzhen. Sound source feature defuzzification method based on end-to-end deep learning[J]. Journal of Vibration and Shock, 2023, 42(21): 133-141

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