基于深度学习的声源无网格定位及量化方法
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渤海造船厂集团有限公司船舶设计研究院 葫芦岛 125000

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TP391

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Meshless Localization and Quantization of Sound Source Based on Deep Learning
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Bohai Shipyard Group Co., Ltd. Huludao 125000, China

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    摘要:

    波束形成技术是一种常用的声源定位方法,但在多数研究中声源强度往往没有考虑到。为准确定位和量化复杂环境下的单一点声源,本文在常规波束形成图的基础上,提出一种基于残差网络的声源定位及其强度估计方法,旨在精确预测点声源的位置和强度。研究采用Acoular软件模拟时间信号,对神经网络进行训练得到预测模型,并通过计算机仿真对深度神经网络能否从麦克风阵列数据中得到单一点源的精确描述进行了验证。结果表明,该方法不仅能够快速准确地给出单一点声源的位置和强度,其中距离误差edist / ∆x ≈ 0.15,水平误差均值,且对于较大的频率有更好的预测效果。

    Abstract:

    Beamforming is a particular method for sound source localization. However, in most of the relating researches, the intensity of the source is often ignored. Therefore, this research proposes a method of sound source localization and intensity estimation based on the residual network based on the conventional beamforming map, aiming to accurately predict the position and intensity of the point sound source. Acoular software is adopted to simulate time signals, the neural network is trained by the simulated signals, and the prediction model can be obtained then. It is verified by computer simulation whether the deep neural network can obtain an accurate description of a single point source from the microphone array data. The results show that the proposed method predicts the location and the intensity of the sound source fast and effectively. Moreover, the proposed method behaves better prediction effect for higher frequencies.

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王言彬,徐长秋,毛富哲.基于深度学习的声源无网格定位及量化方法[J].电子测量技术,2021,44(16):57-61

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  • 在线发布日期: 2024-08-22
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