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- W2140745013 abstract "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 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>st</sup> step, and then on the 2 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>nd</sup> 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." @default.
- W2140745013 created "2016-06-24" @default.
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- W2140745013 date "2011-08-01" @default.
- W2140745013 modified "2023-09-25" @default.
- W2140745013 title "Hierarchical approach for abnormal acoustic event classification in an elevator" @default.
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- W2140745013 doi "https://doi.org/10.1109/avss.2011.6027300" @default.
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