Audio Video Desync Detection Using Machine Learning
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Solution Overview
Problem
Consumers experience dissatisfaction due to audio-visual desynchronization in media content, where the audio stream either leads or lags behind the video stream, and existing technologies lack effective methods to identify and synchronize these streams.
Innovation Solution
The use of machine learning models to identify audio and visual events in media content, allowing for the detection of desynchronization and subsequent synchronization of audio and video streams by analyzing temporal segments and adjusting the search windows based on human perception limits, with optional expansion of search windows for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to identify audio and visual events with high precision, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses machine learning models as intermediary components that automatically identify audio and visual events, replacing manual or rule-based detection methods. These models serve as mediators between raw audio/video data and synchronization detection, improving measurement precision while encapsulating complexity within standardized model interfaces.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based audio-visual event detection with machine learning-based detection systems. This substitution enables more accurate identification of complex audio and visual patterns that would be difficult to detect using conventional methods, thereby improving detection accuracy.
2Reliability
If search windows are expanded to cover larger temporal ranges for detecting desynchronization, then reliability of detection is improved, but loss of time increases
Solution Approach 1:
The patent implements dynamic search window adjustment where the temporal search range is adaptively modified based on detected desynchronization patterns. When desynchronization is detected, the search window expands to cover larger temporal ranges, allowing the system to reliably detect and correct synchronization issues while minimizing unnecessary processing time for synchronized content.
Solution Approach 2:
The system performs preliminary detection using initial search windows to identify potential desynchronization issues before expanding the search range. This preliminary action allows the system to quickly filter out synchronized content and only invest additional processing time when actually needed, balancing reliability with processing efficiency.
3Productivity
If the temporal window size is reduced to match human perception limits, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The patent optimizes the temporal window parameter to align with human perception thresholds for audio-visual desynchronization. By setting the window size to match the minimum perceptible delay, the system achieves efficient processing that focuses computational resources on the critical range where desynchronization actually matters to users, rather than analyzing unnecessarily large time ranges.
Data Source
AI summary
Techniques are described for detecting desynchronization between an audio stream and a video stream.