Robust acoustic directional sensing enabled by synergy between resonator-based sensor and deep learning

Abstract

We demonstrate enhanced acoustic sensing arising from the synergy between resonator-based acoustic sensor and deep learning. We numerically verify that both vibration amplitude and phase are enhanced and preserved at and off the resonance in our compact acoustic sensor housing three cavities. In addition, we experimentally measure the response of our sensor to single-frequency and siren signals, based on which we train convolutional neural networks (CNNs). We observe that the CNN trained by using both amplitude and phase features achieve the best accuracy on predicting the incident direction of both types of signals. This is even though the signals are broadband and affected by noise thought to be difficult for resonators. We attribute the improvement to a complementary effect between the two features enabled by the combination of resonant effect and deep learning. This observation is further supported by comparing to the CNNs trained by the features extracted from signals measured on reference sensor without resonators, whose performances fall far behind. Our results suggest the advantage of this synergetic approach to enhance the sensing performance of compact acoustic sensors on both narrow- and broad-band signals, which paves the way for the development of advanced sensing technology that has potential applications in autonomous driving systems to detect emergency vehicles.