A method for automatic recognition of hazardous acoustic events operating on a super computing cluster is introduced. The methods employed for detecting and classifying the acoustic events are outlined. The evaluation of the recognition engine is provided: both on the training set and using real-life signals. The algorithms yield sufficient performance in practical conditions to be employed in security surveillance systems. The specialized framework for parallel processing of multimedia data streams KASKADA, in which the methods are implemented, is briefly introduced. An experiment intended to assess outcomes of parallel processing of audio data on a supercomputing cluster is featured. It is shown that by employing supercomputing services the time needed to analyze the data is greatly reduced.
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