AI Volume Adjustment for Content-Aware TV Audio Preferences
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Solution Overview
Problem
Existing volume adjustment technologies for electronic devices, such as TVs, fail to account for the type of content being displayed, leading to inconsistent user preferences and an inability to learn and apply preferred volume settings when the same content is reselected, resulting in an unsatisfactory viewing experience.
Innovation Solution
A volume adjusting device and method that includes a reception unit for collecting volume information, a learning unit to correlate volume settings with video content types, and a device control unit to automatically adjust the volume based on learned preferences using a deep neural network model, considering noise levels and user facial information to predict optimal volume settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic volume adjustment is implemented based on noise levels, then volume control convenience is improved, but the ability to reflect user preferences for different content types deteriorates
Solution Approach 1:
The patent segments volume control into two independent components: (1) noise-based automatic volume adjustment for baseline comfort, and (2) content-type-based volume adjustment for preference adaptation. The controller separately processes noise levels and content type identification, then combines both factors to determine final volume settings, allowing each component to function independently while contributing to overall performance.
Solution Approach 2:
The patent merges multiple volume control mechanisms into a unified system that simultaneously considers noise levels, content type, and user preferences. The controller integrates signals from noise detectors, content analyzers, and preference storage to produce a composite volume setting that satisfies both environmental constraints and user preferences across different content types.
2Object-affected harmful factors
If volume is adjusted according to surrounding noise, then listening comfort is improved, but the ability to learn and apply preferred volumes for specific content deteriorates
Solution Approach 1:
The patent implements preliminary action by storing user volume preferences for different content types in advance. When a user manually adjusts volume for a specific content type, the system proactively learns and stores this preference before the same content is encountered again. This pre-learning mechanism ensures that content preference information is captured and preserved, preventing information loss when the same content is reselected.
Solution Approach 2:
The patent establishes a feedback loop where the system continuously monitors user volume adjustments and content being viewed, then uses this feedback to update stored preferences. The controller compares current volume settings with stored preferences, identifies discrepancies, and automatically adjusts future volume settings accordingly, ensuring that listening comfort and preference learning reinforce each other rather than conflict.
3Device complexity
If a unified volume control system is used, then device complexity is reduced, but the ability to provide personalized volume settings for multiple users deteriorates
Solution Approach 1:
The patent implements universality by designing a single controller that performs multiple functions: noise detection, content type identification, user preference storage, and volume adjustment. This multi-functional controller serves all users and all content types through one unified system, avoiding the need for separate control mechanisms for each user while still providing personalized settings through stored preference profiles.
Solution Approach 2:
The patent uses copying by storing replicated preference profiles for different users in the controller's memory. Each user's volume preferences for various content types are copied and stored as separate profiles, allowing the system to retrieve and apply the appropriate profile based on the current user without requiring complex real-time analysis or multiple physical control systems.
Data Source
AI summary
Disclosed are a volume adjusting device and an adjusting method thereof for enabling an electronic device such as a TV to perform a volume adjustment operation among operations executable by the electronic device according to a prediction model stored through machine learning based on communication with surrounding devices in a 5G communication environment. According to the present disclosure, when information indicating adjustment of the volume of an electronic device by a user is generated, volume adjustment information may be learned, and the volume may be automatically adjusted to a volume preferred by the user on the basis of the learned information when the user views a video on the electronic device.