Parameter Inference for Electronic Musical Instruments

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Solution Overview

Problem

Conventional methods for adjusting parameters of electronic musical instruments require significant effort to match user performance tendencies, as they involve per-parameter and per-user adjustments, which is inefficient.

Innovation Solution

A parameter inference method using machine learning to infer assist information for setting instrument parameters based on performance data, allowing for automated parameter setting that aligns with user tendencies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional per-parameter and per-user adjustment methods are used, then parameter settings can be customized to user preferences, but the effort and time required to obtain optimal parameter values increases significantly

Engineering Contradiction:

Improveparameter customizationVSAvoidtime to obtain parameter values

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs self-learning by automatically analyzing performance information and determining optimal parameter values without requiring manual user input or adjustment. The electronic musical instrument autonomously infers assist information related to parameter settings based on analyzed performance data, eliminating the need for users to manually adjust each parameter.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-determines optimal parameter values by analyzing performance information in advance. By inferring assist information from performance data before actual performance, the system prepares optimal settings that can be automatically applied, saving time during actual use.

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If conventional per-parameter adjustment methods are used, then each parameter can be optimized individually, but the overall process becomes complex and labor-intensive

Engineering Contradiction:

Improveparameter optimizationVSAvoidadjustment process complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system merges multiple parameter adjustment tasks into a single automated process. By analyzing performance information comprehensively and inferring assist information that encompasses multiple parameters simultaneously, the system eliminates the need for separate manual adjustments of each parameter, reducing overall complexity while maintaining optimization quality.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system replaces manual mechanical adjustment processes with automated information processing. Instead of physically adjusting each parameter through user interaction, the system uses machine learning models to automatically determine optimal parameter values based on performance data analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If automated parameter setting is implemented, then the effort required is reduced, but the system requires machine learning models and performance data processing infrastructure

Engineering Contradiction:

Improveparameter setting effortVSAvoidsystem infrastructure

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system designs a multi-functional framework where the performance information analysis infrastructure serves multiple purposes: it not only determines optimal parameter values but also provides performance evaluation, skill level assessment, and various other music-related functions. This universal approach justifies the infrastructure complexity by providing multiple benefits beyond simple parameter automation.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20230005458A1Parameter Inference Method, Parameter Inference System, and Parameter Inference Program

Publication Date: 2023.01.05 YAMAHA CORP

AI summary

A parameter inference method realized by a computer, includes obtaining target performance information indicating a performance of music using an electronic musical instrument; inferring assist information from the target performance information with use of a trained inference model generated through machine learning, the assist information being related to setting of a parameter of the electronic musical instrument that conforms to a tendency of the performance; and outputting the inferred assist information related to the setting of the parameter.