Context-Aware Music Segment Tagging and Image Generation
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Solution Overview
Problem
Existing music segment sharing systems lack the ability to provide contextually relevant tags and images, leading to suboptimal search results and user engagement.
Innovation Solution
A system and method that utilizes computer-implemented machine-learning models to generate contextually relevant tags and images for music segments based on metadata, lyrics, and additional contextual information such as artist and historical context, enhancing search relevance and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated tagging and image generation using machine-learning models is implemented, then search relevance and user engagement are improved, but system complexity and computational resources required increase
Solution Approach 1:
The system segments the music analysis process into distinct functional modules: a music analysis model that extracts features from audio segments, a tag generation model that creates descriptive tags, and an image generation model that creates visual representations. Each module operates independently with specialized functions, allowing the system to achieve high search relevance while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between the input music segments and the final tags/images. The music analysis model serves as an intermediary that transforms raw audio into structured feature representations, which then feed into the tag and image generation models. This intermediary processing layer improves measurement precision while distributing system complexity across multiple specialized components.
2Loss of information
If multiple machine-learning models are used for tag and image generation, then contextual relevance is enhanced, but processing time and computational cost increase
Solution Approach 1:
The system performs preliminary analysis by extracting music features and generating context information before creating tags and images. The music analysis model pre-processes the audio segment to identify key characteristics, mood, genre, and temporal features in advance. This preliminary action ensures that subsequent tag and image generation operations have access to comprehensive contextual information, reducing the need for iterative processing and minimizing overall processing time.
Solution Approach 2:
The patent combines multiple information sources and processing streams into unified tag and image generation operations. The system merges music features, metadata, and contextual information into integrated prompts for the language and image generation models. This merging approach maintains high contextual relevance by considering all available information simultaneously, while reducing processing time by eliminating redundant analysis steps.
Data Source
AI summary
A method of automated generation of contextually-relevant images for a music segment includes receiving at least one of basic metadata information and lyric information for the music segment, generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, receiving context information from the computer-implemented machine-learning language model in response to the first prompt, generating a second prompt for the computer-implemented machine-learning language model based on the context information, generating a third prompt by providing the second prompt as an input to the computer-implemented machine-learning language model, and generating an image descriptive of the music segment by providing the third prompt as an input to a computer-implemented machine-learning image generation model.