论文标题
声 - 声明：一种新型的声音定位和定量神经网络
Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification
论文作者
论文摘要
声源定位已应用于不同领域，例如航空和海洋科学，通常使用多个麦克风阵列数据重建源位置。但是，基于模型的波束形成方法无法实现常规光束图的高分辨率。深度神经网络也适合定位声源，但是通常，这些具有复杂网络结构的方法很难被硬件识别。在本文中，提出了一个新的神经网络，称为声学网络，仅使用原始信号来定位和量化声源。实验表明，所提出的方法显着提高了声源预测和计算速度的准确性，这可能会很好地推广到真实数据。代码和训练有素的模型可在https://github.com/joaquinchou/acoustict-net上找到。
Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location. However, the model-based beamforming methods fail to achieve the high-resolution of conventional beamforming maps. Deep neural networks are also appropriate to locate the sound source, but in general, these methods with complex network structures are hard to be recognized by hardware. In this paper, a novel neural network, termed the Acoustic-Net, is proposed to locate and quantify the sound source simply using the original signals. The experiments demonstrate that the proposed method significantly improves the accuracy of sound source prediction and the computing speed, which may generalize well to real data. The code and trained models are available at https://github.com/JoaquinChou/Acoustic-Net.
