Adaptation Quality Evaluation Model for Musical Works
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Solution Overview
Problem
Current methods for evaluating adaptation quality of adapted musical works are inefficient, particularly when dealing with large quantities, as they rely on manual evaluation.
Innovation Solution
A machine learning-based adaptation quality evaluation model is trained using sample features that combine instrument information and musical originality representation values from audio tracks, enabling automated evaluation of adaptation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual evaluation method is used, then evaluation accuracy can be maintained, but evaluation efficiency deteriorates when processing large quantities of adapted musical works
Solution Approach 1:
The patent replaces the manual mechanical evaluation system with an automated machine learning-based evaluation system. The system uses audio track separation to decompose adapted musical works into original and adapted components, then applies trained models to automatically evaluate adaptation quality, substituting human manual review with computational analysis to achieve high-throughput processing while maintaining consistent evaluation standards.
Solution Approach 2:
The patent creates a computational model that learns from manually evaluated samples. By training the evaluation model on a dataset of adapted musical works with known quality scores, the system copies human evaluation expertise into an automated algorithm, enabling it to replicate human judgment at scale without requiring actual human reviewers for each new work.
2Productivity
If automated evaluation model is introduced, then evaluation efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex evaluation task into distinct modular components: audio track separation module, feature extraction module, model evaluation module, and quality scoring module. Each component handles a specific aspect of the evaluation process, making the overall system more manageable and maintainable while enabling parallel processing to improve efficiency.
Solution Approach 2:
The patent develops a universal evaluation framework that can handle multiple types of adapted musical works across different genres and formats. The audio track separation and feature extraction processes are designed to work with various input types, and the trained models can evaluate different adaptation scenarios, reducing the need for separate specialized systems for each case.
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a musical work adaptation quality evaluation model. One of the methods includes: obtaining a plurality of adapted musical work samples; separating audio tracks of each of the plurality of adapted musical work samples; determining instrument information identifying instruments played in each of the audio tracks; performing originality analysis on each of the audio tracks to obtain an originality value corresponding to each audio track; for each of the audio tracks, using a combination of the instrument information and the originality value corresponding to the particular audio track of the adapted musical work samples as a sample feature; and training a musical work adaptation quality evaluation model by using sample features of corresponding adapted musical work samples as input, and a predetermined evaluation score of the corresponding adapted musical work sample as output.