Dynamic Audio Equalization for Threat Detection in Gaming
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Solution Overview
Problem
In gaming scenarios, certain sound frequencies and events can mask the detection of threats, reducing a gamer's response time, as existing systems lack effective methods to enhance sound threat sensitivity without compromising overall audio experience.
Innovation Solution
A system that utilizes machine learning clusters to select and continuously update digital signal processing coefficients for speaker equalization, emphasizing threat sounds by removing low-frequency masking sounds, raising sound pressure levels, and applying harmonic synthesis to enhance human hearing response, thereby improving threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually adjusting sound equalization is used to remove distracting sound events, then threat detection sensitivity is improved, but all sound events are reduced not just distracting ones
Solution Approach 1:
The audio spectrum is segmented into different frequency bands, and the system selectively applies equalization adjustments to specific bands rather than uniformly across all frequencies. This allows threatening sounds in certain frequency ranges to be enhanced while preserving other sound events, resolving the contradiction between improving threat detection and maintaining overall sound information.
2Loss of time
If sound equalization is adjusted to enhance threat sounds, then response time is improved, but the complexity of audio processing increases
Solution Approach 1:
The system pre-calculates and stores equalization profiles for different acoustic scenarios before they are needed during gameplay. When a threat is detected or a scenario is identified, the pre-computed profile is quickly applied, reducing real-time processing complexity while maintaining fast response times for threat detection.
Solution Approach 2:
The patent replaces complex real-time mechanical audio processing with algorithmic and machine learning-based approaches. By using ML models to predict and classify acoustic scenarios, the system simplifies the processing pipeline while achieving enhanced threat sound detection and faster response times.
3Measurement precision
If digital signal processing coefficients are continuously updated, then audio processing accuracy is improved, but computational energy consumption increases
Solution Approach 1:
Instead of continuously updating DSP coefficients at every moment, the system employs periodic updates based on detected acoustic scenario changes. The ML model monitors for significant changes in the acoustic environment and triggers coefficient updates only when necessary, maintaining high processing accuracy while reducing overall computational energy consumption during gameplay.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the gamer's ability to detect threats by prioritizing critical sound events, improving response time through dynamic audio processing that adjusts in real-time to the gaming environment.
Implementation Method 1
raising sound pressure levels
Implementation Method 2
removing low-frequency masking sounds
Implementation Method 3
applying harmonic synthesis to enhance human hearing response
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for improved threat sensitivity in a gaming application. As audio streams are received during a gaming scenario, a threat profile is selected that best matches the audio streams. The threat profile is a machine language cluster of noise used to adjust speaker equalization profile to emphasize threat noises. Digital signal processing (DSP) coefficients of the speaker equalization profile are adjusted as the gaming application continues.