Abstract
Granular particles, filled within an elastic material, produce mechanical vibrations in structures or air when squeezed. This refers to structure-borne noise, is defined as a noise that occurs from the impacts of particles hitting each other due to their momentum. The momentum depends on both properties of particles and velocity of squeezing. Therefore, the structure-borne noise is highly correlated with the properties of particles. In this connection, we study a vibro-tactile sensor for detecting the mechanical vibrations from squeezing granular objects. Specifically, we explore machine learning solutions to detect foreign body within these objects using detected vibrations. We evaluated multiple learning approaches on a collected data set of 900 squeezing experiments across 15 different granular materials. In our experiments, the most successful method was convolutional neural network that achieved an accuracy of 91% on unseen test data. Remarkably, the foreign body was detected with a higher success rate for the majority of material types except salt and coffee granules.