Musicality Classification via Parameter Distance Calculation
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Solution Overview
Problem
Existing methods for evaluating and classifying musicality in performances primarily focus on similarity comparisons between performance data and musical composition data, failing to effectively determine and classify the unique musicality of individual performers.
Innovation Solution
A musicality information provision method and system that calculates distances between performance data using parameters such as time differences, operation strengths, and note lengths to classify musicality, allowing for intuitive determination and grouping of performances based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If performance data is evaluated by simple similarity comparison with reference data, then evaluation process is simple, but musicality classification capability is insufficient
Solution Approach 1:
The patent segments performance data into multiple distinct parameters (time differences, operation strengths, note lengths) to enable comprehensive musicality classification. By dividing the evaluation into parameter-level comparisons rather than holistic similarity assessment, the system achieves precise classification while maintaining manageable complexity through modular processing of each parameter type.
2Measurement precision
If multiple parameters indicating musicality are calculated and compared, then musicality classification precision is improved, but calculation complexity increases
Solution Approach 1:
The patent creates standardized parameter templates (time differences, operation strengths, note lengths) that serve as reference copies for comparison. By establishing predefined parameter structures and calculation methods, the system reduces calculation complexity through reusable templates while maintaining high classification precision through comprehensive parameter coverage.
3Measurement precision
If distance calculation between performance data is performed for classification, then musicality determination accuracy is improved, but information processing time increases
Solution Approach 1:
The patent segments distance calculation into parameter-specific distance computations (time distance, strength distance, length distance) rather than computing a single holistic distance metric. This segmentation enables efficient calculation by processing each parameter independently using optimized distance formulas, reducing overall computation time while maintaining accurate musicality determination through aggregation of parameter-level distances.
Data Source
AI summary
A musicality information provision method includes acquiring first performance data from a performance of a given composition, calculating, with respect to a combination of a plurality of parameters indicating musicality, which are included in the first performance data, respective distances between the first performance data and a plurality of sets of second performance data that are acquired from performances of the given composition and that are compared with the first performance data, and outputting determination information for determining the musicality of the first performance data, the determination information including information indicating the distances.