Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification
This work addresses sound localization for applications in fields like aeronautics and ocean science, offering a more efficient and accurate method compared to existing approaches.
The paper tackles the problem of acoustic source localization by proposing Acoustic-Net, a novel neural network that uses original signals to locate and quantify sound sources, resulting in significant improvements in accuracy and computing speed.
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.