High-resolution raindrop counting via instantaneous frequency sensing on hydrophobic elastic membranes.

In this paper, we introduce a novel approach that paves the way for the creation of affordable, high-precision rainfall sensors utilizing microphone data. The cornerstone of this methodology is an innovative algorithm capable of converting audio recordings into distinctive features, which are subsequently processed by a compact machine learning model. Our findings demonstrate that this technique can attain a temporal resolution of 10 milliseconds with an accuracy of 80%, underscoring its potential to overcome the limitations imposed by the necessity for power infrastructure and specialized expertise in traditional rain sensing methods.