Scene-Aware Volume Control Using Neural Network Feedback
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Solution Overview
Problem
Users face difficulties in adjusting volume settings on electronic devices to suit various environments, leading to suboptimal user experiences, especially in noisy or quiet settings, as existing methods require manual adjustments and lack adaptive intelligence.
Innovation Solution
An electronic device equipped with a processor and memory that uses a trained neural network to identify scenes based on images and sound signals, automatically adjusting volume settings to match the user's preferences in different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual volume adjustment is used, then user control is direct and simple, but the volume settings are inconsistent and require repeated adjustments in varying environments
Solution Approach 1:
The electronic device automatically detects environmental noise levels and scene types using sensors and neural networks, then self-adjusts volume settings without requiring manual user intervention. The system serves itself by monitoring its own operational context and making appropriate volume adjustments based on detected scenarios such as meetings, classrooms, or noisy environments.
Solution Approach 2:
The system continuously monitors environmental noise levels through microphones and sensors, processes this feedback information through neural networks to identify scene types, and uses this feedback to dynamically adjust volume settings. This closed-loop feedback mechanism ensures volume settings remain appropriate as environmental conditions change.
2Adaptability or versatility
If automated scene recognition is implemented, then volume control becomes adaptive and intelligent, but device complexity increases due to neural networks and multiple sensors
Solution Approach 1:
The electronic device uses a multi-functional integrated approach where the same neural network and sensor system serves multiple purposes: detecting noise levels, identifying scene types, determining user presence, and controlling various volume settings simultaneously. This universal system replaces what would otherwise require separate specialized components for each function.
Solution Approach 2:
The patent replaces manual mechanical volume adjustment with an intelligent automated system using neural networks and sensors. The physical manual control interface is substituted with electronic detection and automated processing, where the neural network algorithmically determines appropriate volume settings based on environmental data rather than requiring physical user interaction.
3Measurement precision
If continuous learning and optimization are enabled, then volume settings become increasingly personalized and accurate, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary scene recognition and noise level assessment continuously in the background before user interaction is needed. By pre-processing environmental data and maintaining an understanding of the current scene type, the system reduces the computational time required when actual volume adjustment decisions must be made, as the neural network already has contextual information ready.
Data Source
AI summary
An electronic device includes: a memory storing instructions; and at least one processor configured to execute the instructions stored in the memory to control the at least one processor to: identify a location of the electronic device; obtain an image and a sound signal corresponding to the location; identify, using a trained neural network, a scene where the electronic device is present, based on the image, the sound signal, and the location; and provide settings of the electronic device based on the identified scene.