Acceleration of decision making in sound event recognition employing supercomputing cluster

• A sound event recognition engine on a supercomputing cluster was implemented.
• Dedicated parallel processing framework on the supercomputer was employed.
• The implemented parallel processing approaches was evaluated and compared.
• The decision-making time of sound event recognition was assessed.
• It was proven that parallel processing speeds up the computations.

Parallel processing of audio data streams is introduced to shorten the decision making time in hazardous sound event recognition. A supercomputing cluster environment with a framework dedicated to processing multimedia data streams in real time is used. The sound event recognition algorithms employed are based on detecting foreground events, calculating their features in short time frames, and classifying the events with Support Vector Machine. Different strategies for improving the decision time are introduced. The experiments with the presented strategies are conducted and the results are presented.