In this paper an acoustic surveillance method is proposed for accurately detecting hazardous situations under noisy conditions. In order to improve detection accuracy, the proposed method first tries to separate each atypical event from the input noisy audio signal. Next, maximum likelihood classification using multiple hidden Markov models (HMMs) is carried out to decide whether or not an atypical event occurs. Performance evaluation shows that the proposed method achieves higher detection accuracy under various signal-to-noise ratio (SNR) conditions than a conventional HMM-based method.
(See document for exact affiliation information.)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
