AI Song Finding System for Video Emotion Matching
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Solution Overview
Problem
Video producers face challenges in selecting the best fitting audio material from large databases, as existing methods are time-consuming and often result in songs that are either too short or too long for specific video sections, failing to match the video's emotional and energetic content effectively.
Innovation Solution
A method utilizing AI and expert intelligence to analyze video content, assign emotion and energy tags, and filter a song database to find the best matching songs based on emotion, genre, and length, with optional soft fade-out adjustments for seamless integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual song selection is used, then the producer can carefully evaluate each song, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of the video content, extracting features such as emotion, energy, and scene characteristics before the actual song selection. This advance preparation enables rapid matching against the database without compromising selection quality
Solution Approach 2:
The manual mechanical process of listening to and evaluating songs is replaced with an automated AI system that analyzes video features and matches them with tagged songs in the database, dramatically reducing time while maintaining or improving matching accuracy
2Quantity of substance
If songs are selected from a large database, then more options are available, but it becomes difficult to find the right match
Solution Approach 1:
The system introduces intermediate features such as emotion tags, energy levels, and scene descriptors that act as mediators between the video content and the song database. These intermediaries enable efficient filtering and matching across large databases without requiring direct comparison of all songs
Solution Approach 2:
The song database is segmented and organized by multiple dimensions including emotion, energy, genre, and duration. This segmentation allows the system to quickly narrow down options by applying multiple filtering criteria simultaneously, making large databases manageable and searchable
3Measurement precision
If the producer spends more time selecting songs, then better matches can be found, but productivity decreases
Solution Approach 1:
The time-consuming manual evaluation process is replaced with automated AI analysis that processes video features and matches songs in seconds, maintaining high match quality while dramatically increasing production speed
Solution Approach 2:
The system performs self-service by automatically analyzing video content, selecting appropriate songs, and even adapting songs to match video duration and emotional arcs without requiring extensive manual intervention from the producer
4Manufacturing precision
If songs are manually adapted to video length, then precise timing can be achieved, but the process becomes more complex
Solution Approach 1:
The system automatically adapts song duration and timing to match the video by analyzing video length and emotional arc characteristics. This self-service approach achieves precise timing without requiring complex manual editing operations
Solution Approach 2:
The system performs preliminary song adaptation during the selection process, pre-adjusting song duration and creating fade-in/fade-out effects before the producer receives the final selection, eliminating the need for post-selection timing adjustments
Data Source
AI summary
According to a first embodiment, one method presented herein involves methods of adapting a determined best fitting song from a large audio database for a selected video production. The intrinsic audio of the video material is analyzed for sections containing speech content and the determined best fitting song is adapted in these determined sections. The content of these songs utilized in the instant invention has been tagged by emotion tags describing the energy, the emotion of these songs over time—meaning that each song can contain a plurality of, even overlapping, emotions. Further, in some embodiments the song's volume will be adjusted downward during intervals when speech is present in the intrinsic audio.