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Recently,the Internet of Things (IoT) has attracted more and more attention.Multimedia sensor network plays an important role in the IoT,and audio event detection in the multimedia sensor networks is one of the most important applications for the Internet of Things.In practice,it is hard to get enough real-world samples to generate the classifiers for some special audio events (e.g.,car-crashing in the smart traffic system).In this paper,we introduce a TrAdaBoost-based method to solve the above problem.By using the proposed approach,we can train a strong classifier by using only a tiny amount of real-world data and a large number of more easily colle cted samples (e.g.,collected from TV programs),even when the real-world data is not sufficient to train a model alone.We deploy this approach in a smart traffic system to evaluate its performance,and the experiment evaluations demonstrate that our method can achieve satisfying results.
Recently, the Internet of Things (IoT) has attracted more and more attention.Multimedia sensor network plays an important role in the IoT, and audio event detection in the multimedia sensor networks is one of the most important applications for the Internet of Things. practice, it is hard to get enough real-world samples to generate the classifiers for some special audio events (eg, car-crashing in the smart traffic system) .In this paper, we introduce a TrAdaBoost-based method to solve the above problem .By using the proposed approach, we can train a strong classifier by using only a tiny amount of real-world data and a large number of more easily colle cted samples (eg, collected from TV programs), even when the real-world data is not sufficient to train a model alone. We deploy this approach in a smart traffic system to evaluate its performance, and the scientific evaluations demonstrate that our method can achieve satisfaction results.