Information Processing Device for Synchronizing Musical Performances
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Solution Overview
Problem
Existing technologies lack the ability to effectively estimate and synchronize the temporal relationships between multiple performances in ensemble scenarios, such as musical pieces, leading to challenges in controlling automatic performances to follow actual performances naturally.
Innovation Solution
An information processing method and device that utilize a trained model to generate change parameters for the temporal relationship between two motions by inputting time-series data, employing an autoregressive process and convolutional neural networks to approximate and control the temporal errors between performance parts, allowing for natural synchronization of performances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a plurality of performers perform in parallel in ensemble scenarios, then the performances become synchronized through nonverbal interaction, but it becomes difficult to estimate and control the temporal relationships among the performances
Solution Approach 1:
The patent introduces an information processing device as an intermediary that automatically estimates temporal relationships among multiple performers using machine learning models. This mediator analyzes performance data and generates temporal relationship information without requiring direct human intervention to measure or control the synchronization, thus resolving the contradiction between achieving reliable synchronization and managing the complexity of estimation.
Solution Approach 2:
The patent replaces manual or mechanical methods of measuring and controlling temporal relationships with an automated information processing system using machine learning. The system substitutes complex manual analysis with algorithmic processing that can handle multiple performance streams simultaneously, reducing the practical complexity while maintaining reliable synchronization estimation.
2Ease of operation
If an automatic performance instrument executes in parallel with actual performance, then the automatic performance can follow the actual performance, but existing technologies lack the ability to accurately estimate temporal changes
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using performance data before actual use. The system performs offline learning and model training in advance, so that when the automatic performance instrument runs in parallel with actual performance, the temporal relationship estimation is already optimized. This preliminary preparation enables precise real-time tracking without adding complexity to the live performance control.
Solution Approach 2:
The patent implements feedback mechanisms where the information processing device continuously monitors actual performance data and adjusts temporal relationship estimates in real-time. The system uses feedback from performance deviations to refine its predictions, enabling the automatic performance instrument to accurately follow the actual performance by constantly adapting to temporal changes based on observed deviations.
3Measurement precision
If machine learning models are used to estimate temporal relationships, then the estimation accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex task of temporal relationship estimation into multiple specialized machine learning models, each handling specific aspects of performance data analysis. The system divides performance data into different streams and uses dedicated models for different types of temporal relationship analysis, making the overall complex system manageable through modular organization of processing functions.
Solution Approach 2:
The patent creates a universal information processing device that handles multiple functions: data collection, preprocessing, model training, temporal relationship estimation, and output generation. This multi-functional system consolidates various processing tasks into a single integrated platform, reducing the need for separate specialized systems and managing complexity through functional integration.
Data Source
AI summary
An information processing method includes generating a change parameter relating to a process in which a temporal relationship between a first motion and a second motion changes, by inputting, into a trained model, first time-series data that represent a content of the first motion, and second time-series data that represent a content of the second motion in parallel to the first motion.