A Fast Sound Source Mapping by Morphological Operations on Acoustic Images

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on the microphone array is proved effective in various acoustic imaging applications. Generally, DAMAS and its variations result in heavy computation load due to the nature of large-scale linear equations and the iterative solver, which prevent the deployment of DAMAS to platforms with limited resources, such as the edge devices of the internet of things (IoT). In order to enhance the computational efficiency of DAMAS, a fast algorithm based on DAMAS with grid compression by the morphological operations on the acoustic images is proposed in this work. The proposed approach intentionally neglects the physics behind the acoustic imaging, but emphasizes the general visual features of acoustic images, as if they were natural images. A low computation load can be guaranteed regardless of the complicated acoustic environments, which alternatively ensures the robustness of proposed algorithm. Numerical simulations demonstrate that the proposed algorithm effectively accelerates the acoustic image reconstruction. In practical experiments, the proposed method reduces the algorithm time to be within 26% of DAMAS. In certain scenarios, both the algorithm time and localization accuracy of the proposed method outperform the conventional methods.