Audio Noise Reduction Selection Module for Dynamic Environments
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Solution Overview
Problem
Information handling systems face challenges in effectively suppressing non-stationary noise interference, which affects the quality of audio applications such as video conferencing and speech communications by failing to accurately identify and mitigate various noise sources in dynamic environments.
Innovation Solution
An audio noise reduction selection computing module that performs calibration and configuration based on contextual data, including user, system, and environmental settings, to generate a configuration policy for selecting and applying appropriate audio noise reduction models to minimize non-stationary noise sources through steady-state monitoring and rule-based actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional noise suppression methods are used, then the system structure remains simple, but the noise suppression effectiveness deteriorates in dynamic environments
Solution Approach 1:
The patent implements dynamic adaptation by continuously monitoring contextual inputs (location, time, environment, audio sources) and switching between different noise reduction models based on real-time conditions. This allows the system to maintain high noise suppression effectiveness across varying environments without requiring an overly complex fixed architecture, as the complexity is managed through dynamic selection rather than simultaneous execution of all models.
Solution Approach 2:
The patent segments the noise reduction functionality into multiple specialized models (e.g., model for train noise, model for crowd noise, model for office environments). Each model handles specific noise types, allowing the system to maintain simplicity by loading and executing only the relevant segment needed for the current context, rather than running all possible noise reduction algorithms simultaneously.
2Reliability
If multiple noise reduction models are executed simultaneously, then noise suppression effectiveness improves, but computational resources and processing time increase
Solution Approach 1:
Instead of executing all available noise reduction models simultaneously (excessive action), the system applies partial action by selecting and executing only the specific model or combination of models relevant to the current contextual situation. The configuration policy determines the minimal necessary computational action required for effective noise suppression given the detected environment, audio sources, and user settings, thereby reducing unnecessary computational resource consumption.
Solution Approach 2:
The system dynamically adjusts the set of active noise reduction models based on real-time contextual monitoring. When the environment changes (e.g., detecting train noise vs. office ambient noise), the system transitions between different model configurations, ensuring that computational resources are allocated efficiently to handle only the currently relevant noise types rather than maintaining all models active continuously.
3Speed
If the system responds quickly to noise changes, then user experience improves, but the accuracy of noise identification may deteriorate
Solution Approach 1:
The system performs preliminary action by continuously monitoring contextual inputs and pre-identifying potential noise sources based on environmental patterns and audio characteristics before full noise suppression is activated. This allows the system to anticipate noise changes and prepare appropriate models in advance, enabling quick response while maintaining identification accuracy through pre-analysis of the operational context.
Solution Approach 2:
The system implements feedback mechanisms where the results of noise reduction processing are monitored and fed back into the contextual analysis. This feedback loop allows the system to adjust its noise identification and model selection in real-time, ensuring both rapid response to noise changes and continuous improvement of identification accuracy based on actual processing outcomes and user experience metrics.
Data Source
AI summary
Selecting audio noise reduction models for noise suppression in an information handling system (IHS), including performing calibration and configuration of an audio noise reduction selection model, including: identifying contextual data associated with contextual inputs to the IHS; training, based on the contextual data, the audio noise reduction selection model, including generating a configuration policy including configuration rules, the configuration rules for performing actions for selection of a combination of audio noise reduction models to reduce combinations of noise sources associated with the IHS; performing steady-state monitoring of the IHS, including: monitoring the contextual inputs of the IHS, and in response, accessing the audio noise reduction selection model, identifying configuration rules based on the monitored contextual inputs, applying the configuration rules to select a particular combination of audio noise reduction models, applying particular combination of audio noise reduction models to reduce a particular combination of noise sources associated with the IHS.