Audio Video Synchronization Error Detection via Frame Comparison
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Solution Overview
Problem
Current media content synchronization methods are inefficient and labor-intensive, often resulting in synchronization errors between audio and video that can confuse viewers and detract from the media experience, as they rely on manual input and analysis of the entire media duration.
Innovation Solution
A synchronization feature implemented by a service provider computer that identifies potential conversation segments in media content by analyzing sound levels and facial images, allowing for automatic detection and correction of synchronization errors without requiring full media content analysis, using algorithms and facial recognition techniques to compare audio and video frames and modify encoding or metadata for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input methods are used to identify synchronization errors, then measurement precision can be maintained, but productivity and ease of operation deteriorate due to labor intensity
Solution Approach 1:
The system automatically detects synchronization errors by analyzing audio and video streams without requiring manual intervention. The computer system performs self-service analysis by comparing audio frames with video frames, extracting features such as lip movement and sound waves, and automatically identifying mismatches between them.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational algorithms. Instead of human operators visually inspecting media content, the system uses computer vision and audio processing algorithms to automatically detect synchronization errors, substituting mechanical human labor with automated digital processing.
2Measurement precision
If manual analysis of entire media duration is performed, then measurement precision is maintained, but loss of time increases due to labor-intensive processing
Solution Approach 1:
The system segments the media content into discrete frames - both audio frames and video frames - and processes them individually or in small groups. This segmentation allows the system to efficiently compare corresponding frames without analyzing the entire media duration at once, reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary processing by extracting and pre-processing audio and video features before synchronization analysis. By pre-extracting features such as lip movement patterns and sound wave characteristics, the system prepares the data in advance for rapid comparison, reducing the time needed for final synchronization error detection.
3Productivity
If automatic detection algorithms are implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The system introduces intermediary processing layers that simplify the complexity management. By using intermediate feature extraction modules that convert complex audio and video data into simplified representations (such as lip movement vectors and audio energy spectra), the system manages complexity through structured intermediate representations rather than direct complex analysis.
Data Source
AI summary
Techniques for identifying synchronization errors between audio and video are described herein. Audio portions in audio for media content may be identified based at least in part on a sound level associated with first respective segments of the audio portions. A subset of the audio portions may be selected based at least in part on a duration associated with the audio portions. For a segment of the subset a first number of frames in the audio and a second number of frames in the video for the segment may be determined. A determination may be made that the segment includes a conversation segment based at least in part on the first number of frames, the second number of frames, and a first threshold. A synchronization error may be identified in the conversation segment based on a difference between the audio and the video of the conversation segment.