AI Multimedia Production for Music-Video Matching and Image Selection
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Solution Overview
Problem
Existing short video applications struggle with recommending Professional Generated Content (PGC) music that does not fit the user's video scene, requiring manual and time-consuming editing to create multimedia works like Music Videos (MVs).
Innovation Solution
A production method using AI algorithms to determine the matching degree between user-selected audio and multimedia information, select high-quality images, and synthesize them into a multimedia work, leveraging neural networks for audio and video understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual selection and editing of music and video clips is performed, then the matching quality between music and video content is improved, but the time cost and technical threshold increase
Solution Approach 1:
The system automatically performs music-video matching without requiring manual user intervention. The neural network model analyzes video labels and music attributes, then autonomously selects and edits clips to create the final multimedia work, eliminating the need for users to manually browse and edit content.
Solution Approach 2:
The patent replaces manual mechanical editing operations with an automated neural network-based system. Instead of users manually selecting clips and adjusting timing, the system uses AI algorithms to automatically match music with video content based on semantic understanding of both modalities.
2Adaptability or versatility
If a wide selection range of music is provided, then user choice is improved, but the ability to match music type with video scene picture fit deteriorates
Solution Approach 1:
The system provides personalized music recommendations tailored to each user's specific video content. Instead of offering a generic wide selection, the neural network analyzes the unique characteristics of each video label and music track, then selects music that locally optimizes the match for that specific video-scene combination.
Solution Approach 2:
The system dynamically adjusts music selection parameters based on video label analysis. The neural network processes video attributes (such as scene type, mood, rhythm) and modifies music selection criteria accordingly, changing the matching parameters to align with the specific video content characteristics.
Data Source
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AI summary
A production method and device for multimedia works, and a computer-readable storage medium. The method comprises: acquiring a target audio and at least one piece of multimedia information, calculating the degree of match between the target audio and the multimedia information, sorting multimedia information on the basis of the degree of match in a descending order, assigning top-ranking multimedia information as target multimedia information; calculating the image quality of each image in the target multimedia information, sorting every image of the target multimedia information on the basis of image quality in a descending order, assigning the top-ranking images as target images; and synthesizing a multimedia work on the basis of the target images and the target audio. The method allows the acquisition of high-definition multimedia works in which the video content and background music match with each other, and reduces the time costs and costs of learning that a user spends on video editing.