Electronic Musical Instrument Intensity Correction via Trained Model

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Solution Overview

Problem

User playing habits in electronic musical instruments make it difficult to determine appropriate intensity characteristics for controlling sound intensity, as existing techniques fail to accurately account for individual playing styles.

Innovation Solution

An information processing system that includes a processor and memory to acquire user playing habit data, generate correction data using a trained model, and correct intensity characteristics to match playing intensity with sound intensity, allowing for personalized sound output.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional intensity characteristic control methods are used, then the system can generate musical sound intensity, but it cannot accurately account for individual user playing styles

Engineering Contradiction:

Improveadaptability to user playing habitsVSAvoidprecision of intensity characteristic determination

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system acquires actual playing data from users, processes this feedback information through a trained model, and uses the results to generate correction data that adjusts intensity characteristics. This closed-loop feedback mechanism enables the system to continuously adapt to individual playing styles while maintaining precise intensity control.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes the parameters of intensity characteristics by generating correction data that adjusts the relationship between playing intensity and sound intensity. The trained model processes playing habit data to produce parameter adjustments that personalize the intensity characteristics for each user.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If a trained model is introduced to learn playing habits, then adaptability to user playing styles improves, but system complexity increases

Engineering Contradiction:

Improveadaptability to user playing habitsVSAvoidcomplexity of processing system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary action by training a machine learning model in advance to learn the relationship between playing habits and appropriate intensity corrections. This pre-trained model can then quickly process new playing data without requiring complex real-time analysis, reducing operational complexity while maintaining high adaptability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The trained model acts as an intermediary between raw playing data and intensity characteristic adjustments. It mediates the complex relationship between playing habits and sound intensity by translating playing patterns into correction data, simplifying the overall system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230386439A1Information processing system, electronic musical instrument, information processing method, and training model generating method

Publication Date: 2023.11.30 YAMAHA CORP

AI summary

An electronic musical instrument is configured to: (a) acquire input data that includes habit data indicative of a playing habit of a user in playing a musical instrument; (b) generate correction data by inputting the acquired input data into at least one trained model that learns a relationship between training input data and training correction data; and (c) correct, using the generated correction data, at least one first intensity characteristic representative of a relationship between: (i) a playing intensity in playing the musical instrument by the user; and (ii) a sound intensity of a musical sound output in response to playing of the musical instrument.