Context-Dependent Audio Volume Compensation Using Microphone Feedback
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Solution Overview
Problem
Existing audio devices lack the ability to automatically adjust volume levels based on context, such as environmental noise and user activity, leading to suboptimal listening experiences.
Innovation Solution
A method and system for context-dependent automatic volume compensation using a wearable device that analyzes environmental conditions and user interactions to dynamically adjust volume levels through a processor-controlled volume compensator, employing various volume compensation models based on sensor data and audio content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual volume control is used, then user can precisely control volume level, but user must continuously adjust volume based on environmental conditions
Solution Approach 1:
The system performs automatic volume compensation by itself without requiring user intervention. The processor continuously monitors audio signals and environmental conditions, then automatically adjusts volume levels using compensation models, making the system self-regulating based on real-time conditions
Solution Approach 2:
The system uses feedback from audio signals and environmental sensors to continuously monitor conditions and adjust volume accordingly. The processor analyzes the audio signal characteristics and environmental noise levels, then applies appropriate compensation models to maintain optimal listening levels dynamically
2Adaptability or versatility
If fixed volume compensation model is used, then system complexity is reduced, but adaptability to different contexts is poor
Solution Approach 1:
The system dynamically selects and switches between different volume compensation models based on the detected context. The processor evaluates environmental conditions and audio characteristics in real-time, then chooses the most appropriate compensation model from multiple available models, making the system adaptive without requiring a completely complex redesign
Solution Approach 2:
The volume compensation functionality is divided into multiple specialized models, each optimized for specific contexts (e.g., noisy environments, quiet environments, different audio types). This segmentation allows each model to be relatively simple while the collection provides comprehensive adaptability across various scenarios
3Adaptability or versatility
If multiple volume compensation models are maintained, then context adaptability is improved, but device complexity increases
Solution Approach 1:
Multiple volume compensation models are pre-configured and stored in the device memory before runtime. The processor simply needs to select from these pre-prepared models based on detected context, rather than dynamically generating complex models in real-time, reducing computational complexity while maintaining adaptability
Data Source
AI summary
A method performed by a programmed processor of an electronic device. The device obtains an audio signal, obtains, using one or more microphones, a microphone signal that includes audio of an environment in which the electronic device is located. The device determines a context of the device, and selects a volume compensation model from several models based on the determined context. The device processes the audio signal according to the selected volume compensation model and the microphone signal, and uses the processed audio signal to drive one or more speakers of the device.