Note Sequence Analysis for Automatic Music Evaluation
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Solution Overview
Problem
Existing music evaluation technologies require users to repeatedly select a target piece of music for evaluation, which is cumbersome and inconvenient, especially for frequent practice sessions.
Innovation Solution
A note sequence analysis method and apparatus that calculates a similarity index between user-designated notes and pre-stored reference notes, automatically selecting a target piece of music for evaluation, thereby simplifying the user's task and reducing the need for repeated music selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the user designates a piece of music each time for evaluation, then the evaluation accuracy is improved, but the operation complexity and time consumption increase
Solution Approach 1:
The system automatically selects reference music pieces by analyzing the played note sequence and comparing it with stored reference pieces, eliminating the need for manual user designation. The system serves itself by autonomously identifying the most likely reference piece based on similarity calculations.
Solution Approach 2:
Multiple reference music pieces are pre-stored in the system before evaluation begins. By having reference data ready in advance, the system can quickly perform similarity comparisons without requiring the user to select or prepare reference material at the time of evaluation.
2Measurement precision
If the user designates a piece of music each time for evaluation, then the evaluation accuracy is improved, but the time consumption increases
Solution Approach 1:
Reference music pieces are pre-stored and pre-processed into note sequences before evaluation begins. This preliminary preparation allows the system to perform rapid similarity comparisons during actual evaluation, significantly reducing the time required per evaluation session.
Solution Approach 2:
The system automatically performs the time-consuming task of identifying and selecting reference music pieces through algorithmic similarity analysis, freeing the user from this time-intensive manual process while maintaining evaluation accuracy.
3Extent of automation
If multiple reference music pieces are pre-stored, then the automation capability is improved, but the device complexity increases
Solution Approach 1:
The manual mechanical process of user selection is replaced with an automated computational system that calculates similarity indices between played note sequences and stored reference pieces. This substitution enables automatic selection while managing complexity through algorithmic processing rather than complex hardware or manual procedures.
Data Source
AI summary
A note sequence analysis method calculates a similarity index based on similarity between a designated sequence of notes designated by a user and a reference sequence of notes for each of a plurality of reference pieces of music, then selects a reference piece of music from among the plurality of reference pieces of music based on the similarity index calculated for each of the plurality of reference pieces of music, and specifies an evaluation index of the designated sequence of notes based on the similarity index calculated for the selected reference piece of music.