Audio Trailer Generation With Genre-Specific Mood Segment Selection
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Solution Overview
Problem
Existing trailer generation systems for media items, particularly audio content, require manual input and struggle to capture the stylistic qualities of the media item, making it difficult to create trailers that accurately represent the mood or energy of the content.
Innovation Solution
A system that uses a parallel neural network to automatically identify segments from an audio file that best capture the vibe or emotion of the podcast, combining these segments with additional portions to generate a trailer that reflects the overall stylistic qualities of the media item.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trailer generation is used, then the trailer can be customized to reflect specific content, but the process requires user input and time investment
Solution Approach 1:
The system performs self-service by automatically analyzing audio content, identifying stylistic qualities, and generating trailers without requiring user input. The neural network model processes the audio file, extracts relevant segments, and assembles them into a trailer automatically, eliminating the need for manual user involvement while maintaining quality.
Solution Approach 2:
The patent replaces the mechanical manual process with an automated neural network-based system. Instead of requiring human operators to listen to and select audio segments, the system uses deep learning models to automatically identify and select segments that capture the stylistic qualities of the content.
2Productivity
If simple summary generation is used, then the process is fast and automated, but the trailer cannot capture the stylistic qualities or mood of the content
Solution Approach 1:
The system segments the audio file into multiple smaller clips and analyzes each segment individually to identify stylistic qualities. This segmentation allows the neural network to precisely identify and select segments that represent the mood and energy of the content, rather than treating the entire audio file as a single unit.
Solution Approach 2:
The system changes the parameter of analysis from simple content summary to stylistic quality detection. By using neural networks to detect and measure stylistic parameters such as mood, energy, and genre characteristics, the system can generate trailers that accurately represent the content's stylistic qualities while maintaining automated processing.
3Productivity
If automated trailer generation without genre criteria is used, then the process is simple and fast, but the trailer cannot be optimized for specific podcast genres
Solution Approach 1:
The system introduces dynamic adaptability by allowing the genre criteria to be adjusted based on the specific podcast genre. The neural network model can be configured with different stylistic quality parameters for different genres (e.g., true crime, comedy, educational), enabling the system to automatically adapt its analysis and selection criteria to match the specific genre requirements while maintaining automated processing.
Data Source
AI summary
An electronic device receives an audio file and divides the audio file into a plurality of segments of audio. The electronic device automatically, without user input, determines, for each respective segment of audio, a descriptor from a plurality of descriptors and a value of the descriptor for the segment. The electronic device selects one or more segments of audio, less than all, of the plurality of segments of audio, based on a comparison of the respective values of respective descriptors for respective segments and genre-specific criteria selected based on a genre of the audio file. The electronic device generates a summarized version of the audio file by arranging the selected one or more segments of audio into a sequence of the one or more segments.