Video Event Segmentation for Context-Aware Audio Alignment
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Solution Overview
Problem
The process of enhancing audio in multimedia is time-consuming, labor-intensive, and limited in creative options due to manual event identification, challenging audio matching, and suboptimal automated processes, which affects efficiency and cost in various industries.
Innovation Solution
A system and method for multimedia audio enhancement through video file segmentation, event extraction, and contextual data structuring, utilizing a processor and memory to parse video files, extract events, and associate them with audio objects, enabling efficient matching and generation of audio based on event ontology and vector embedding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual event identification is used, then accuracy of event definition is improved, but production time and labor cost increase
Solution Approach 1:
The system enables self-service automated event identification using AI models that automatically parse video files, extract events, generate descriptions, and create ontology data without requiring manual audio engineer intervention for these tasks
Solution Approach 2:
Manual mechanical processes of event identification and audio matching are replaced with automated AI-based systems including event extraction models, ontology determination modules, and vector embedding engines that process video and audio data computationally
2Manufacturing precision
If comprehensive audio matching criteria are evaluated, then audio quality and narrative reinforcement are improved, but the complexity and time of audio selection increase
Solution Approach 1:
The system transforms multiple qualitative audio matching criteria into quantifiable parameters including vector embeddings, ontology classifications, temporal alignment metrics, and similarity scores that enable automated computational comparison and selection
Solution Approach 2:
An automated audio matching system acts as an intermediary between the video content and audio library, evaluating multiple criteria simultaneously and presenting ranked options to the user, thereby simplifying the selection process while maintaining comprehensive evaluation
3Productivity
If automated audio processes are used, then production efficiency is improved, but accuracy and creative control decrease
Solution Approach 1:
The system provides feedback loops where automated event extraction and audio matching results are presented to users for review, correction, and refinement, allowing iterative improvement of accuracy while maintaining automated efficiency
Solution Approach 2:
The system combines dynamic automated processing for initial event identification and audio matching with flexible user control points where creators can adjust parameters, select from ranked options, and refine results to maintain creative control throughout the workflow
Data Source
AI summary
Disclosed are a method, a device, and/or a system of audio enhancement of video through video file segmentation, event extraction, and contextual data structuring for efficient matching, generation, and/or alignment of audio to a depicted event. In one embodiment, a system includes a memory storing computer readable instructions that when executed initiate a video object in a database representing a video file and store a video segmentation reference drawn from the video object to a segmentation object, which may represent a shot or scene in the video. The system may parse the video file to extract an event including an event range, an event description, and an event ontology, and may generate encoding vector(s) therefrom. The system may initiate an event object, then link the event object to the video object through the segmentation object, to enable efficient import of context for audio matching and/or audio generation for the event.