Acoustic Event Detection Using Double-Layer Classification

Abstract

The world’s elderly population is expected to grow by more than triple by 2050. This indicates that detecting human activity is needed to prevent emergency situations for people living alone. Sound is an excellent resource because it has enough information to detect events and it is easy to gather. However, variability is one of the main challenges in research related to sound. To solve variability, most research focuses on selecting sound features because researches related to sound have their own purpose and suitable sound features are different for each research. In this research, we focus on classifiers to solve problem of variability. The Double-layer classification (DLC) is composed with Support Vector Machines (SVM) and the Viterbi search and detects sound events using the Hidden Markov Model (HMM). As a result, unusual sounds which occur at home such as a baby crying, a scream, a breaking glass, and a gunshot are classified by the DLC, which accuracy is 94.4%. ⓒ 2014 DGIST