Normal and Abnormal Non-Speech Audio Event Detection Using MFCC and PR-Based Feature Sets

Abstract

Non-speech audio event detection and classification has become a very active subject of research, since it can be implemented in many important areas: audio surveillance and context awareness systems. In this study, non-speech normal and abnormal audio events were detected by Mel-frequency cepstrum coefficients (MFCC) and Pitch range (PR) based features using artificial neural network (ANN) classifiers. We have 4 abnormal events (glass breaking, dog barking, scream, gunshot) and 2 normal events (engine noise and rain). Event detection, using ANN classifiers, resulted in an accuracy of up to 92%, with recognition rates overall in the range of 78%-87.5%.