Music Feature Extraction for AI Composition Control
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Solution Overview
Problem
Conventional AI music composition systems primarily rely on visual cues, limiting music producers who require specific musical features like chord progressions and bass lines, leading to a demand for more tailored music information generation.
Innovation Solution
An information processing apparatus that stores music feature information extracted from existing music, using machine learning to generate music that matches the desired features, allowing producers to select and compose music based on specific musical elements such as chord progressions, melodies, and bass progressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI music composition uses general visual cues (bright/dark settings), then general users can easily create music, but producers cannot obtain specific musical features (chord progressions, bass lines) they need
Solution Approach 1:
The patent segments music composition into multiple independent feature dimensions including chord progression, bass progression, melody, and rhythm. Each dimension can be independently selected and controlled, allowing producers to specify exact musical features while maintaining ease of use through modular selection interfaces.
Solution Approach 2:
The patent transitions from traditional 2D visual mood settings (bright/dark) to multi-dimensional music feature control by adding dimensions for chord progression, bass lines, melody patterns, and rhythm. This dimensional expansion enables precise musical feature specification while maintaining user-friendly operation through structured selection menus.
2Loss of information
If AI generates music based on limited visual parameters, then the system remains simple to operate, but the music information lacks specific features required by producers
Solution Approach 1:
The patent pre-extracts and stores multiple music feature dimensions (chord progression, bass progression, melody, rhythm) from existing music data before composition. This preliminary preparation allows the system to quickly retrieve and combine specific musical features during composition without adding operational complexity, thereby reducing information loss while maintaining system simplicity.
Solution Approach 2:
The patent changes the parameter space of music generation from simple visual adjectives (bright/dark) to detailed musical parameters (chord progression sequences, bass line patterns, melody contours, rhythm structures). This parameter transformation enables comprehensive music feature information retention while managing system complexity through standardized parameter structures.
Data Source
AI summary
An information processing apparatus according to the present disclosure includes: a storage unit that stores a plurality of pieces of music feature information in which a plurality of types of feature amounts extracted from music information is associated with predetermined identification information, the music feature information being used as learning data in composition processing using machine learning; a reception unit that receives instruction information transmitted from a terminal apparatus; an extraction unit that extracts the music feature information from the storage unit according to the instruction information; and an output unit that outputs presentation information of the music feature information extracted by the extraction unit.