Neural Network Synchronization Error Localization in Media Titles
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Solution Overview
Problem
Existing algorithmic techniques for identifying synchronization errors in media titles fail to determine specific times of errors, leading to difficulties in correction, and are often inaccurate, resulting in missed errors or false positives.
Innovation Solution
A neural network system is implemented to identify synchronization errors by using a convolutional subnetwork to generate feature maps from audio and video features, an attention module to compute weight values indicating misalignment likelihood, and an output layer to pinpoint misaligned audiovisual blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If algorithmic techniques are used to identify synchronization errors, then detection capability is provided, but specific timing information is not determined and accuracy is poor
Solution Approach 1:
The patent segments the media title into multiple blocks, where each block contains a specific time range of video and audio data. The neural network processes each block independently to generate feature maps and identify synchronization errors at the block level, thereby obtaining precise timing information while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the synchronization error detection problem from traditional algorithmic analysis into a multi-dimensional neural network processing task. By using convolutional neural networks to generate feature maps across time and feature dimensions, the system achieves both accurate detection and precise timing localization that single-dimensional algorithms cannot provide.
2Reliability
If traditional algorithmic techniques are used, then processing is simpler, but synchronization errors are missed or false positives occur
Solution Approach 1:
The patent replaces traditional algorithmic techniques with a neural network system. The convolutional neural network uses learned features and attention mechanisms to automatically identify synchronization errors, replacing manual rule-based algorithms with an adaptive intelligent system that reduces false positives and missed detections despite increased complexity.
Solution Approach 2:
The patent introduces feature maps as an intermediary between the raw audio-video data and the final synchronization error detection. The convolutional neural network generates feature maps that capture temporal and spectral characteristics, serving as an intermediate representation that enables more reliable error identification compared to direct algorithmic comparison.
Data Source
AI summary
A neural network system that is trained to identify one or more portions of a media title where synchronization errors are likely to be present. The neural network system is trained based on a first set of media titles where synchronization errors are present and a second set of media titles where synchronization errors are absent. The second set of media titles can be generated by introducing synchronization errors into a set of media titles that otherwise lack synchronization errors. Via training, the neural network system learns to identify specific visual features included in one or more video frames and corresponding audio features that should be played back in synchrony with the associated visual features. Accordingly, when presented with a media title that includes synchronization errors, the neural network can indicate the specific frames where synchronization errors are likely to be present.