Harmonics Learning System Using Reinforcement Learning Scoring
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Solution Overview
Problem
Existing sheet music generation technologies face difficulties in generating harmonics education materials that follow exact harmonic rules, making it challenging to automate the process of completing voice parts in music education.
Innovation Solution
A harmonics learning system that uses a communication unit to provide and receive sheet music parts, a model generation unit that generates a scoring model through reinforcement learning using divided vertical and horizontal rules, and a control unit to mark harmonic scores, allowing for systematic and quantitative learning of harmonic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing sheet music generation technologies are used, then sheet music can be generated quickly, but the generated sheet music does not follow exact harmonic rules
Solution Approach 1:
The system employs reinforcement learning where the scoring model provides feedback on whether generated sheet music follows harmonic rules. The model generates sheet music, evaluates it against harmonic rules, and iteratively improves by learning from the evaluation results, thus achieving both speed and rule compliance.
Solution Approach 2:
The scoring model automatically evaluates and refines generated sheet music without human intervention. The system self-corrects by using the scoring model's feedback to improve future generations, making the process autonomous while ensuring harmonic rule adherence.
2Manufacturing precision
If manual harmonics education methods are used, then exact harmonic rules can be taught, but the process is time-consuming and not automated
Solution Approach 1:
The patent replaces manual music theory evaluation with an automated scoring model based on reinforcement learning. This artificial intelligence system substitutes the mechanical process of manual harmonics analysis, providing accurate rule-based evaluation at high speed without human intervention.
Solution Approach 2:
The system transforms qualitative harmonic rules into quantifiable scoring parameters. By converting musical theory concepts into measurable metrics that the scoring model can evaluate, the system maintains academic rigor while enabling automated processing and high-speed education delivery.
3Measurement precision
If a comprehensive scoring model with many rules is created, then harmonic evaluation accuracy improves, but system complexity increases
Solution Approach 1:
The scoring model divides complex harmonic rules into separate, manageable components that can be independently evaluated. This segmentation allows the system to maintain high evaluation accuracy through multiple specialized rules while keeping each individual rule simple and the overall system tractable.
Data Source
AI summary
According to one embodiment, provided is a harmonics learning system comprising: a communication unit for providing, to a user terminal, sheet music of at least one first voice part, and receiving, from the user terminal, sheet music of at least one second voice part that excludes the first voice part; a model generation unit for generating a scoring model by using a plurality of rules divided into vertical elements and horizontal elements; and a control unit for marking harmonic scores with the sheet music of the first voice part and the sheet music of the second voice part by using the scoring model.