International Business Machines Corporation
LIVE EVENT CONTEXT BASED CROWD NOISE GENERATION

Abstract:

Event data and event participant data corresponding to a time during an event are encoded into a multidimensional feature vector using a trained encoder network. Using an attention mask, the multidimensional feature vector is adjusted, the adjusting amplifying a portion of the multidimensional feature vector according to an importance level of the portion. The adjusted multidimensional feature vector is decoded into an excitement level score using a trained decoder network. Using the excitement level score and a trained neural network model, a frequency and an amplitude of simulated crowd noise corresponding to the time during the event are generated.