Musical Instrument Practice System Personalizing Feedback via Habit Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for assisting users in playing musical instruments do not effectively improve playing skills as they fail to account for individual user playing habits, leading to ineffective practice methods.
Innovation Solution
An information processing system that acquires user playing data and generates habit data by comparing it to trained models learning from reference music data, identifying practice phrases tailored to the user's habits to enhance practice efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic playing assistance is provided based on statistical analysis of playing data, then the system is simple to implement, but it fails to address individual user playing habits and mistakes
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing playing data from multiple users to identify common mistakes and playing habits before providing assistance. The mistake pattern database is pre-populated with statistical information about typical errors, allowing the system to quickly match individual user patterns without complex real-time analysis.
Solution Approach 2:
The patent introduces an intermediary component - the mistake pattern database - that mediates between raw playing data and personalized feedback. This database stores statistical information about common mistakes and serves as a reference for comparing individual user performance, enabling personalization without requiring complex AI or machine learning systems.
2Productivity
If personalized practice phrases are provided based on individual playing habits, then practice effectiveness is improved, but the time required to analyze and process playing data increases
Solution Approach 1:
The system performs preliminary analysis by pre-processing playing data to identify mistake patterns and categorize them into a database. This preliminary action allows the system to quickly retrieve and match relevant practice phrases without performing complex analysis during each practice session, significantly reducing real-time processing time.
Solution Approach 2:
The system extracts specific mistake patterns from overall playing data and separates them into a dedicated mistake pattern database. By extracting only the relevant error information rather than processing complete playing sessions, the system reduces data processing time while maintaining personalization effectiveness.
3Measurement precision
If statistical analysis is performed on playing data without considering individual habits, then the system provides general feedback, but it does not effectively help users improve their specific playing weaknesses
Solution Approach 1:
The system applies local quality by providing customized feedback tailored to each user's specific mistake patterns rather than generic advice. The mistake pattern database stores localized information about specific errors (e.g., wrong notes, timing issues, fingering problems) that allows the system to target practice phrases to the user's particular weaknesses, improving assessment accuracy.
Solution Approach 2:
The system performs preliminary analysis to extract and store individual playing habit information in the mistake pattern database before providing feedback. This preliminary action preserves individual habit information by pre-processing and categorizing it, ensuring that personalized assessment accuracy is achieved without losing important individual characteristics during data transmission or storage.
Data Source
AI summary
An information processing system (a) acquires user playing data indicative of playing of a piece of music by a user, (b) generates habit data indicative of a playing habit of the user in playing the piece of music on a musical instrument, by inputting the acquired user playing data into at least one first trained model that learns a relationship between (i) player playing training data indicative of playing of a piece of reference music by a player, and (ii) corresponding training habit data indicative of a playing habit of the player in playing the piece of reference music on a musical instrument, the playing habit being indicated by the player playing training data; and (c) identifies a practice phrase based on the generated habit data.