Audio-Video Controller Using Two-Stage Filtration for Change Detection
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Solution Overview
Problem
Current systems for controlling audio-video content output struggle to accurately detect changes in live feeds, leading to potential faults such as frozen or irrelevant camera views, and fail to differentiate between meaningful and irrelevant content segments, resulting in false positives and negatives.
Innovation Solution
A controller system that analyzes audio-video streams to produce mood vectors, employing a two-stage filtration process involving a low pass filter and a Hysteresis filter to identify 'chapter' points, allowing for rapid detection of faults and enabling the retrieval of alternative content from a store or switching to different programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-stage filtration process is used to detect changes in audio-video content, then the system complexity is reduced, but the measurement precision and reliability of change detection deteriorates, leading to false positives and negatives
Solution Approach 1:
The filtration process is divided into two distinct stages: a first-stage filter that performs initial smoothing of the audio-video feature data, and a second-stage filter that performs threshold-based detection. This segmentation allows each stage to specialize in a specific function, improving overall detection accuracy while maintaining manageable complexity at each stage.
Solution Approach 2:
The first-stage filter acts as an intermediary between the raw audio-video data and the second-stage threshold detector. It processes the intermediate results and prepares them for final detection, allowing the system to achieve high precision without requiring the final detection stage to handle raw, unprocessed data directly.
2Productivity
If rapid detection of faults in live feeds is implemented, then the productivity and response time are improved, but the reliability of detection may worsen due to increased false positives from rapid processing
Solution Approach 1:
The first-stage filter performs preliminary smoothing and processing of the audio-video feature data before it reaches the second-stage threshold detector. This preliminary action prepares the data in advance, allowing rapid threshold-based detection in the second stage without sacrificing reliability, as the intermediate processing has already filtered out noise and stabilized the signals.
3Measurement precision
If the system distinguishes between meaningful and irrelevant content segments, then the measurement precision of change detection is improved, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The detection system is segmented into two functional stages: content analysis (first-stage filter) and change detection (second-stage filter). This segmentation allows the system to differentiate between meaningful and irrelevant content segments in the first stage, then apply simple threshold-based detection in the second stage, achieving high precision without proportionally increasing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects changes in audio-video feeds, reducing false positives and negatives, enabling timely correction of faults and seamless transitions between content segments, thereby ensuring consistent and high-quality output.
Implementation Method 1
The controller is arranged to operate a two-stage filtration process on the vector. The first stage involves operating a low pass filter on the vector.
Implementation Method 2
The second stage involves operating a Hysteresis filter on the output of the low pass filter to eliminate noise.
Data Source
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AI summary
A controller for controlling the output of an audio-video feed has an input for receiving audio-video content and a vector engine arranged to produce a continuous metadata value of M dimensions at intervals from the audio-video content. The metadata value may indicate aspect of the video content such as rate, pace, tone or other such information. A low pass filter and hysteresis filter are then provided to filter the continuous metadata value to produce a binary output. The binary output is provided to a retrieval module arranged to control the audio-video content in dependence upon the binary output. In this way, automated segmentation of audio-video content may be reliably achieved.