Virtual Spectator Emotion Amplification via Inverse Bias Mitigation
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Solution Overview
Problem
Current extended reality systems fail to accurately replicate the emotional experience of live events for virtual spectators, as they struggle with bias detection and class imbalance in user reactions, leading to an incomplete and unfair representation of crowd emotions.
Innovation Solution
A computer-implemented method that uses machine learning models to analyze virtual spectators' emotions, retrieve matching historical sound clips, apply inverse bias mitigation, and perform adversarial debiasing to generate a standardized sound representation, which is then output to create a realistic virtual crowd experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to detect emotions of virtual spectators, then the virtual experience accuracy is improved, but bias detection and class imbalance in user reactions cause incomplete and unfair representation of crowd emotions
Solution Approach 1:
The system performs preliminary actions by retrieving historical sound clips that match current event situations before generating the final audio output. This allows the system to pre-process and prepare emotionally representative audio segments, which are then adjusted through bias mitigation techniques to ensure fair and accurate representation of crowd emotions.
Solution Approach 2:
The system applies parameter changes by using inverse bias mitigation to amplify underrepresented emotions and adversarial fairness debiasing to adjust the emotional distribution parameters. This transforms the biased emotion detection output into a more balanced and fair representation of crowd reactions, resolving the contradiction between detection accuracy and representation fairness.
2Reliability
If historical sound clips are retrieved and processed through machine learning models with bias mitigation, then the fairness of crowd emotion representation is improved, but the processing time and system complexity increase
Solution Approach 1:
The system uses copying by retrieving historical sound clips that replicate past crowd reactions to similar event situations. Instead of generating emotions from scratch, the system copies and adapts pre-existing emotionally representative audio segments, which simplifies the processing while maintaining fairness through bias mitigation techniques.
Solution Approach 2:
The system introduces an intermediary layer between emotion detection and audio generation by applying inverse bias mitigation and adversarial fairness debiasing as intermediate processing steps. This intermediary processing ensures fairness while managing system complexity through structured, modular transformation of the audio data.
3Reliability
If real-time emotion amplification and debiasing are applied to historical sound clips, then the immersion of virtual spectators is improved, but the processing speed and real-time performance may be affected
Solution Approach 1:
The system performs preliminary action by retrieving and pre-processing historical sound clips before they are needed for real-time output. This advance preparation allows the computationally intensive bias mitigation and emotion amplification processes to occur beforehand, reducing the real-time processing burden while maintaining high immersion quality.
Solution Approach 2:
The system uses copying of historical sound clips as a foundation for real-time processing. By copying and adapting pre-existing audio segments rather than generating emotions in real-time, the system achieves both high immersion quality through detailed processing and acceptable real-time performance through efficient adaptation of pre-processed material.
Data Source
AI summary
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.