Object Classification from Analysis of Impact Acoustics

Abstract

We address the problem of autonomously classifying objects from the sounds they make when struck, and present results from different attempts to classify various items. We extract the two most significant spikes in the frequency domain as features, and show that accurate object classification based on these features is possible. Two techniques are discussed: a minimum-distance classifier and a hybrid minimum-distance/decision-tree classifier. Results from classifier trials show that object classification using the hybrid classifier can be done as accurately as using the minimum-distance classifier, but at lower computational expense.