Video-Audio Synchronization Using Latent Alignment Across Dynamic Rates
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Solution Overview
Problem
Existing commercial solutions for video and audio (V/A) synchronization in media production are insufficient for scale and capability, leading to manual detection of synchronization errors, which are burdensome for quality control teams, and existing academic models alter the original content or require frame rate conversion, introducing artifacts.
Innovation Solution
A convolution-free V/A synchronizer model using modality-specific Transformers for encoding raw video and audio into latent representations, with time-aware positional encoding and contrastive learning, allowing for synchronization without CNNs and handling varying frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used for V/A synchronization errors, then detection capability is maintained, but productivity and quality control efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical detection processes with an automated machine learning model that uses contrastive learning to detect V/A synchronization errors. The model automatically compares audio and video features to identify misalignment, eliminating the need for manual quality control while maintaining detection accuracy.
Solution Approach 2:
The system performs self-detection of synchronization errors through automated feature extraction and comparison. The machine learning model independently identifies V/A misalignment issues without requiring human intervention, enabling the system to self-correct and self-monitor quality across media content.
2Extent of automation
If existing academic models are used for V/A synchronization, then synchronization capability is achieved, but content fidelity deteriorates due to frame rate conversion and content alteration
Solution Approach 1:
The patent changes the approach from temporal parameter adjustment (frame rate conversion) to feature space transformation. The model extracts features from audio and video at their original frame rates and performs synchronization in the feature space using contrastive learning, avoiding any alteration of the original content while achieving synchronization.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that operates in feature space rather than directly manipulating the original audio-video signals. This intermediary layer allows synchronization to be achieved through learned feature relationships without altering the source content, preserving content fidelity.
3Device complexity
If fixed frame rate processing is used, then processing simplicity is maintained, but adaptability to varying media content deteriorates
Solution Approach 1:
The patent implements a dynamic feature extraction approach where the model adapts to varying frame rates and content characteristics. The contrastive learning framework dynamically adjusts to different media formats and resolutions, allowing the system to handle diverse content without requiring fixed preprocessing parameters.
Solution Approach 2:
The machine learning model is designed with universal feature extraction capabilities that work across different frame rates, resolutions, and media types. The contrastive learning approach creates a unified framework that can process various content formats without requiring separate processing pipelines for each format.
Data Source
AI summary
A system includes a hardware processor and a memory storing a video/audio (V/A) synchronizer including video and audio encoders. The hardware processor executes the V/A synchronizer to receive raw video and audio extracted from media content, partition the raw video into video frame patches, partition the raw audio into audio samples, pre-process the video frame patches and the audio samples for encoding. The hardware processor further executes the V/A synchronizer to encode, using the video encoder, the pre-processed video frame patches to provide pre-processed and encoded video frame patches used to provide a latent representation of the raw video, encode, using the audio encoder, the pre-processed audio samples to provide pre-processed and encoded audio samples used to provide a latent representation of the raw audio, and synchronize, using the latent representations of the raw video and the raw audio, the raw audio with the raw video.