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http://dx.doi.org/10.7776/ASK.2014.33.4.248

Frequency-Cepstral Features for Bag of Words Based Acoustic Context Awareness  

Park, Sang-Wook (고려대학교 전기전자전파공학과)
Choi, Woo-Hyun (고려대학교 전기전자전파공학과)
Ko, Hanseok (고려대학교 전기전자전파공학과)
Publication Information
The Journal of the Acoustical Society of Korea / v.33, no.4, 2014 , pp. 248-254 More about this Journal
Abstract
Among acoustic signal analysis tasks, acoustic context awareness is one of the most formidable tasks in terms of complexity since it requires sophisticated understanding of individual acoustic events. In conventional context awareness methods, individual acoustic event detection or recognition is employed to generate a relevant decision on the impending context. However this approach may produce poorly performing decision results in practical situations due to the possibility of events occurring simultaneously or the acoustically similar events that are difficult to distinguish with each other. Particularly, the babble noise acoustic event occurring at a bus or subway environment may create confusion to context awareness task since babbling is similar in any environment. Therefore in this paper, a frequency-cepstral feature vector is proposed to mitigate the confusion problem during the situation awareness task of binary decisions: bus or metro. By employing the Support Vector Machine (SVM) as the classifier, the proposed feature vector scheme is shown to produce better performance than the conventional scheme.
Keywords
Acoustic context awareness; Acoustic event detection or recognition; Bag of Words (BOW); Support Vectors Machine (SVM);
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