Music Generation Feedback Loop for User-Guided Regeneration
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Solution Overview
Problem
Existing music generation technologies using machine learning models often fail to meet user needs due to limitations in model performance and accuracy of text descriptions, resulting in suboptimal music output.
Innovation Solution
A music generation method that allows users to adjust automatically generated music through an agent-guided process, enabling regeneration based on user input to improve music quality and alignment with user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning model is used for automatic music generation, then generation efficiency is improved, but music quality and user satisfaction deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the agent analyzes user interactions with generated music and uses this feedback to refine future generation. The agent monitors user preferences, adjustment operations, and satisfaction levels, then incorporates this information into the generation process to improve quality while maintaining efficiency.
Solution Approach 2:
The patent introduces an agent as an intermediary between the machine learning model and the user. This agent mediates by interpreting user needs, generating initial music, analyzing user feedback, and refining the output. The agent handles the complexity of quality improvement while the underlying model maintains efficient automatic generation.
2Manufacturing precision
If text description accuracy is improved, then music generation precision is improved, but user input complexity increases
Solution Approach 1:
The agent performs self-service by automatically analyzing user interactions, identifying patterns in preferences, and generating refined prompts without requiring users to manually adjust text descriptions. The system serves itself by learning from user behavior to improve precision automatically.
Solution Approach 2:
The patent changes the parameters of text description by using the agent to generate refined, more accurate prompts based on user interactions. Instead of requiring users to provide precise descriptions, the system transforms simple user inputs into optimized prompts that achieve high generation precision.
Data Source
Figure 1~2B
Figure 2C~2D
Figure 3A~3B
AI summary
The present disclosure relates to a music generation method, music generation apparatus and computer-readable storage medium, and falls into the field of computer technology. The music generation method includes: receiving a first prompt information input by a user, the first prompt information comprising descriptive information of music; generating a first musical work according to the first prompt information, the first musical work comprising a first music; and generating a second musical work according to an adjustment operation of the user on the first musical work, the adjustment operation comprising at least one of an adjustment operation on the first prompt information or an adjustment operation on the first music.