Hierarchical approach for abnormal acoustic event classification in an elevator

In this paper, we propose a hierarchical method to detect and classify abnormal acoustic events occurring in an elevator environment. The Gaussian Mixture Model (GMM) based event classifier essentially employs two types of acoustic features; Mel Frequency Cepstral Coefficient (MFCC) and Timbre. We explore the effectiveness of various combinations of the two features in terms of classification performance. In addition, we design a hierarchical approach for realizing acoustic event classification and compare it with a single-level approach. It can be verified from an experiment, that the classification performance is improved when the proposed hierarchical approach is applied. In particular, for detection of abnormal situations, we employ a maximum likelihood estimation approach for acoustic event recognition at the 1^st step, and then on the 2^nd step we determine the abnormal contexts by using the ratio of abnormal events to cumulative events during a certain period. For performance evaluation, we employ a database collected in an actual elevator under several scenarios. By experimental results, our proposed method demonstrates 91% correct detection rate and 2.5% error detection rate for abnormal context.