Musical Performance Analysis Using Learned Model for Sound-Production Point Estimation
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Solution Overview
Problem
Existing technologies for analyzing musical performances struggle to accurately estimate the point when a musical performance is started by a player, as they rely on detecting a cueing action after a predetermined period, which may not accurately reflect the player's sound-production timing.
Innovation Solution
A musical performance analysis method and apparatus that utilize a learned model to estimate the sound-production point based on time series feature data from a player's actions, using image data and machine learning to predict the point of sound production with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a predetermined period is waited after detecting a cueing action to estimate the sound-production point, then the estimation method is simple, but the accuracy of estimating the sound-production point deteriorates
Solution Approach 1:
The system performs preliminary detection of cueing actions and collects action data within a reference period before the estimated sound-production point. This preliminary data collection enables more accurate estimation by analyzing the temporal relationship between the cueing action and the actual sound production, rather than simply waiting a predetermined period.
Solution Approach 2:
The patent replaces the simple time-delay mechanism with a learned model that processes action data. The learned model analyzes the temporal patterns and relationships in the action data to estimate the sound-production point, substituting mechanical time-waiting with intelligent data-driven prediction.
2Measurement precision
If action data within a reference period is used with a learned model to estimate the sound-production point, then the estimation accuracy improves, but the processing complexity increases
Solution Approach 1:
The learned model serves multiple functions: it detects cueing actions, analyzes temporal patterns in action data, and estimates the sound-production point. This multi-functionality reduces the need for separate processing systems while achieving high estimation accuracy through unified intelligent analysis.
Data Source
AI summary
An apparatus is provided that accurately estimates a point at which a musical performance is started by a player. The apparatus includes the musical performance analysis unit 32, and the musical performance analysis unit 32 obtains action data that includes a time series of feature data representing actions made by a player during a musical performance for a reference period and estimating a sound-production point based on the action data at an estimated point using a learned model L.