Abnormal detection based on dynamic programming and Bayesian networks

This paper treats abnormal noise detection in audio streams from rolling bearing as a maximization task, where the solution is obtained by means of dynamic programming. The proposed method seeks the sequence of segments and respective class labels, i.e., abnormal noises vs. all other audio types, that maximize the product of posterior class label probabilities, given the segments' data. It is the first time that Bayesian networks is applied in the detection of abnormal noise, the method first estimates required posterior probabilities by combining soft classification decisions from a set of Bayesian Network combiners. Experiments that have been performed on a large set of audio streams indicate that the proposed method yields high performance in terms of both precision and recall of detected abnormal noise.