Fourier Domain Kernel Density Estimation-based Approach for Hail Sound classification

In this study, a method that recognizes the sound of hail is proposed for a system designed to minimize the damage caused by hail to vehicles. The designed system uses signal processing and machine learning. The sounds received by a microphone in the vehicle were converted into frequency space and the kernel density estimation of the frequency values occurring in a certain time interval (approximately 2 seconds) was obtained. This is based on the prediction that the histogram of the frequency of hail falling on the car can have a defining characteristic. In this context, it has been designed to create a two-class machine learning problem, including full sound samples and ambient sound samples. A solution to the machine learning problem was sought with the Support Vector Machines (SVM) algorithm. The SVM algorithm was chosen due to its simplicity and fast working dynamics. While learning is offline in the SVM algorithm, testing is done online. Related software was implemented using MATLAB. In experimental studies, we collected a dataset where almost 500 hail sound segments were used and similarly 400 ambient sound segments were collected. A hold out cross validation approach with various split ratio values are used. It has been seen that the proposed method predicts hail sounds with 92.22% accuracy when the hold out cross validation ration is 90% and 10%.