Video Mood Analysis for Audio Onset Matching
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Solution Overview
Problem
Users face difficulties in finding suitable digital media content that relates to each other, such as video and audio content, due to inaccurate or missing metadata tags, making it challenging for online repositories to suggest compatible media for playback.
Innovation Solution
A system that analyzes video files to identify shot transition timings and entities, uses a natural language model to determine associated moods, and selects audio files with matching moods and audio onset distances to suggest suitable audio playback for the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metadata tagging is used for media content, then the system is simple to operate, but the accuracy of media content matching is poor
Solution Approach 1:
The system performs preliminary analysis of video content by detecting shot transitions and identifying entities before audio matching occurs. This advance processing creates a foundation of extracted features (timing information, entity tags, mood classifications) that enable more accurate matching without requiring complex real-time processing during the actual matching operation.
Solution Approach 2:
The video analysis process is divided into distinct segments: shot transition detection, entity identification, mood classification, and audio feature extraction. Each segment handles a specific aspect of the analysis, allowing the system to manage complexity through modular processing while achieving comprehensive and accurate media matching.
2Measurement precision
If comprehensive video analysis is performed to identify shot transitions and entities, then audio matching accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts and stores timing information from shot transitions and entity appearances in advance, creating a pre-processed feature set that can be quickly queried during audio matching. This preliminary extraction of temporal and semantic features enables accurate matching without requiring intensive real-time analysis.
Solution Approach 2:
The system transforms video content into different parameter representations suitable for audio matching, such as converting visual entity data into mood classifications and timing patterns. This parameter transformation allows the system to work with optimized data formats that reduce processing requirements while maintaining matching accuracy.
3Adaptability or versatility
If an online media repository provides detailed media analysis, then media recommendation quality is improved, but the difficulty of operation increases
Solution Approach 1:
The system automatically performs video analysis, entity recognition, and audio matching without requiring user intervention. Users simply need to upload or select video content, and the system handles all complex processing steps autonomously, including shot transition detection, entity identification, and audio file selection, thereby maintaining ease of operation while delivering high-quality recommendations.
Solution Approach 2:
The system introduces an intermediary processing layer that translates between user-friendly operations and complex technical analysis. This intermediary automatically converts simple user actions (like selecting a video) into detailed technical processes (shot analysis, entity recognition, mood classification) and back into simple results (audio recommendations), shielding users from complexity while enabling sophisticated functionality.
Data Source
AI summary
Methods for suggesting an audio file for playback with a video file using an analysis of objects in images from the video file are provided. In one aspect, a method includes receiving a selection of a video file, identifying shot transition timings in the video file, and analyzing each shot transition associated with the identified shot transition to a identify an entity within the respective shot transition. The method also includes providing an identification of the identified entities to a natural language model to identify at least one mood associated with the identified entities, selecting, from a collection of audio files, at least one audio file associated with the at least one mood and including an average audio onset distance within an audio onset distance threshold, and providing an identification of the at least one audio file as a suggestion for audio playback with the video file. Systems and machine-readable media are also provided.