Application of abnormal sound recognition system for indoor environment

Abstract

In our living environment, there are various types of sounds. According to the uniqueness of sounds, people can further comprehend the surrounding by the sense of hearing. Nowadays, voice recognition had been widely applied in various applications. In this paper, we proposed an abnormal sound recognition system for monitoring indoor sounds. Twenty-four features were extracted from each sound frame. The sequential floating forward selection (SFFS) was then adopted to select high discriminative features. The support vector machine (SVM) was finally used to classify the sounds into six categories (screaming, infants' crying, coughing, glass breaking, laughing and doorbell ringing). From the experiment results, the proposed system can effectively recognize different kinds of abnormal sounds with a high recognition rate.