Audio Transparency Mode for Selective Noise Alerting
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Solution Overview
Problem
Information handling systems fail to effectively isolate and notify users of specific noise sources in environments while maintaining an immersive productivity mode, as existing noise isolation methods do not allow for user-defined exceptions or notifications for important sounds.
Innovation Solution
An audio transparency mode is implemented in information handling systems, which involves calibrating an audio noise source identification model to identify and configure rules for specific noise sources, allowing users to select and receive notifications for important sounds through the audio output or associated devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If noise isolation is implemented to eliminate distractions, then user focus and productivity are improved, but users cannot hear or be notified of important environmental sounds
Solution Approach 1:
The system extracts and separates specific noise sources from the general environmental noise using audio analysis. Important sounds are identified and extracted from the mixed audio environment, allowing them to be processed differently from background noise. This enables selective transparency where only relevant sounds break through the noise isolation.
Solution Approach 2:
The patent applies different quality treatments to different portions of the audio spectrum and different noise sources. Rather than uniform noise isolation, the system applies transparency selectively to specific frequency ranges or noise sources that are deemed important, while maintaining isolation for other noises. This local differentiation resolves the contradiction between isolation and awareness.
2Productivity
If all noise sources are blocked to create immersive environment, then user immersion is improved, but system complexity increases due to need for sophisticated noise classification
Solution Approach 1:
The system performs preliminary configuration and training to establish noise source profiles and user preferences before actual use. During this setup phase, the system learns which noise sources are important to the specific user and configures相应的 transparency rules. This preliminary action reduces the complexity of real-time classification by having pre-established criteria.
Solution Approach 2:
The patent implements machine learning models that automatically adapt and improve noise classification based on user feedback and usage patterns. The system self-trains and refines its noise source identification without requiring manual configuration for each new noise type, reducing the effective complexity burden on the user while maintaining sophisticated classification capabilities.
3Loss of information
If noise transparency is provided for all sounds, then users can hear important noises, but user focus and productivity are reduced due to constant distractions
Solution Approach 1:
The system applies transparency partially rather than fully - only to the extent necessary for user safety and awareness. By using configurable thresholds and selective noise source transparency, the system provides just enough noise information to prevent missing important sounds while maintaining sufficient isolation to preserve workflow continuity. This partial action resolves the contradiction between awareness and focus.
Data Source
AI summary
Performing an audio transparency mode of an information handling system (IHS), including performing a calibration and configuration of an audio noise source identification model, including: training the audio noise source identification model, including generating a configuration policy including configuration rules for performing computer-implemented actions for providing a particular noise source through an audio output of the IHS, providing a notification of the particular noise source to devices associated with the user, or both; performing a steady-state monitoring of the IHS, including: identifying a particular noise source of environment of the IHS; and in response: identifying configuration rules of the audio noise source identification model based on the identified particular noise source, applying the configuration rules to perform computer-implemented actions to provide the particular noise source through the audio output of the IHS, provide a notification of the particular noise source to the devices associated with the user, or both.