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- W2167155152 abstract "In this paper, we describe an Acoustic Event Recognition (AER) system for locating events of interest in the audio stream of multimedia recordings. We focus on two non-speech acoustic events; bomb explosions and gunfire, which typically exist in surveillance videos and are of importance in monitoring and alerting applications. Recognition is performed using a discriminative approach based on Support Vector Machines (SVM). We compare the new approach to a baseline system that utilizes a Hidden Markov Model (HMM)-based classification approach. We performed experiments on a corpus of publicly available video files containing gunfire and explosion events. Our results show that the new discriminative approach, when configured to use a rich combination of acoustic features, achieves a high retrieval precision at a notable recall under noisy conditions. As compared to HMM-based system, we achieved 54% relative improvement in F-score for explosion recognition with 1.5% relative improvement in F-score for gunfire recognition." @default.
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- W2167155152 date "2011-08-01" @default.
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- W2167155152 title "Discriminative Acoustic Event Recognition in multimedia recordings" @default.
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