Abstract: A live event virtual experience is provided. Emotions of virtual spectators to a current situation occurring in a live event at a physical venue are determined from received data regarding reactions of the virtual spectators to the live event using bias detection. A historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is retrieved. The historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is input into a machine learning model that performs inverse bias mitigation to amplify bias and applies in-process adversarial fairness debiasing. The historical sound clip after performing the inverse bias mitigation to amplify the bias and applying the in-process adversarial fairness debiasing is converted to a standardized historical sound segment length. A sound representation is generated from the standardized historical sound segment length.

Abstract: A live event virtual experience is provided. Emotions of virtual spectators to a current situation occurring in a live event at a physical venue are determined from received data regarding reactions of the virtual spectators to the live event using bias detection. A historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is retrieved. The historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is input into a machine learning model that performs inverse bias mitigation to amplify bias and applies in-process adversarial fairness debiasing. The historical sound clip after performing the inverse bias mitigation to amplify the bias and applying the in-process adversarial fairness debiasing is converted to a standardized historical sound segment length. A sound representation is generated from the standardized historical sound segment length.

Abstract: An interaction involving two or more users is identified based on at least one biometric value of a biometric attribute of one user of the two or more users exceeding a threshold value of the biometric attribute. A determination is made that the identified interaction is a stressful interaction for the one user. At least one de-escalation recommendation meant to reduce the intensity of the determined stressful interaction is transmitted to the one user.