Set-Top Box Audio Adaptation Through Real-Time Stream Genre Analysis
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Solution Overview
Problem
Existing set-top boxes require manual user intervention for sound rendering adjustments, which is restrictive and unreliable, often deterring inexperienced users and failing to adapt to broadcast streams effectively.
Innovation Solution
A set-top box with a processing unit that performs real-time multimodal analysis on audio and video data sources to automatically adjust audio playback settings based on genre recognition using classification models like transformers and convolutional neural networks, optimizing sound rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual user intervention is required for audio mode selection, then the system can adapt audio parameters to broadcast content, but user experience deteriorates due to restricted operation and complexity
Solution Approach 1:
The system performs automatic audio parameter adaptation without requiring user intervention. The processing unit analyzes the broadcast stream and autonomously selects and applies appropriate audio parameters based on content type, eliminating the need for users to manually configure audio settings while maintaining effective adaptation to different broadcast contents
Solution Approach 2:
The system pre-configures multiple audio parameter sets corresponding to different broadcast content types before playback occurs. When a broadcast stream is received, the system quickly selects and applies the pre-prepared audio parameters matching the content type, avoiding real-time manual configuration while ensuring rapid and accurate audio adaptation
2Adaptability or versatility
If manual audio mode selection is used, then audio parameters can be adjusted, but reliability deteriorates due to user inexperience and inconsistent adaptation
Solution Approach 1:
The system autonomously performs audio parameter selection based on automated stream analysis, eliminating variability introduced by user inexperience. The processing unit consistently applies the same analysis criteria and parameter selection logic, ensuring reliable and repeatable audio adaptation across different users and broadcast contents
Solution Approach 2:
The system continuously monitors the broadcast stream characteristics and adjusts audio parameters accordingly. This feedback mechanism ensures that audio parameters are dynamically optimized based on actual stream content, maintaining high reliability in adaptation even when broadcast conditions change or users have varying levels of expertise
3Ease of operation
If automatic audio adaptation is implemented, then ease of operation improves, but device complexity increases due to real-time analysis requirements
Solution Approach 1:
The system pre-establishes multiple audio parameter sets and classification models before actual playback. This preliminary preparation allows the system to quickly match and apply appropriate parameters during broadcast, reducing real-time processing complexity while maintaining automatic operation and ease of use
Solution Approach 2:
The audio adaptation process is divided into distinct segments: stream analysis, content classification, parameter selection, and audio processing. This segmentation allows each component to be optimized independently, reducing overall system complexity while enabling automatic operation through modular processing stages
4Reliability
If real-time multimodal analysis is performed, then adaptation speed and reliability improve, but energy consumption increases
Solution Approach 1:
The system performs preliminary analysis of broadcast streams to classify content type before applying audio processing. By pre-classifying the stream genre and selecting appropriate parameter sets in advance, the system reduces the computational burden during actual audio processing, lowering energy consumption while maintaining high recognition accuracy
Solution Approach 2:
The system applies real-time multimodal analysis selectively based on broadcast content characteristics and user needs. Rather than continuously performing maximum-complexity analysis, the system adjusts the level of analysis to the minimum necessary for accurate genre recognition, reducing overall energy consumption while maintaining sufficient reliability for effective audio adaptation
Data Source
AI summary
A set-top box includes a setting module configured to carry out and/or control analyses in real time on at least two distinct data sources relating to the input stream, the data sources being selected among metadata associated with the input stream, a current audio signal coming from the input audio signal, and, if the input stream also comprises an input video signal, at least one target image coming from the input video signal, and define, on the basis of the results of these analyses, a genre of the input stream, the genre being associated with audio parameters; and a configuration module which dynamically adapts, using the audio parameters, a setting of an audio playback device incorporated into or connected to the set-top box and comprising at least one loudspeaker.