Music Feature Extraction for Producer-Controlled AI Composition
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Solution Overview
Problem
Conventional AI-based music composition systems primarily cater to general users, offering limited options for setting music features like chord progression and bass progression, failing to meet the specific needs of professional producers who require music information that aligns with their creative intentions.
Innovation Solution
An information processing apparatus that extracts multiple types of feature amounts from music information, associates them with identification information as learning data, and uses machine learning to generate music that matches the desired features, allowing producers to select and compose music based on specific elements such as chord progression and lyric input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI-based music composition systems provide only general user interfaces with limited feature settings, then the system is easy to operate for general users, but it cannot meet the specific needs of professional producers who require detailed control over chord progression, bass progression, and other music features
Solution Approach 1:
The system segments music composition control into multiple independent feature dimensions (chord progression, bass progression, melody, rhythm, etc.), allowing users to selectively control specific aspects rather than treating music composition as a monolithic process. This enables professional producers to precisely control only the features they need while maintaining ease of operation.
Solution Approach 2:
The system dynamically adapts its interface and control granularity based on user needs. Professional producers can access detailed feature controls when needed, while the system maintains a simplified interface for general users. The composition model can also dynamically adjust generated music features based on the specific parameters the user has controlled.
2Productivity
If AI composition systems generate music based on general images like bright and dark, then the composition process is simple and fast, but the generated music lacks specific stylistic features and professional quality
Solution Approach 1:
The system changes the control parameters from general aesthetic descriptors (bright/dark) to specific music theory parameters (chord progression sequences, bass line patterns, rhythmic structures). This allows the AI to generate music with precise stylistic features while maintaining efficient automated composition processes.
Solution Approach 2:
The system replaces manual music composition mechanics with AI-based generative models that can process and generate music features automatically. The AI learns from training data to understand the relationship between control parameters and music features, enabling automated generation of professional-quality music with specific stylistic characteristics.
3Manufacturing precision
If the system extracts and processes multiple types of feature amounts from music information, then the generated music can match specific producer requirements, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary extraction and organization of music features during the training phase, building pre-computed feature representations that can be efficiently queried and applied during composition. This reduces the computational burden during actual music generation while maintaining high precision in matching producer requirements.
Solution Approach 2:
The system introduces an intermediate representation layer that bridges the gap between raw music information and the AI composition model. This intermediate layer organizes and standardizes multiple feature types (chord progression, bass progression, melody contours) into a unified format that the AI can process efficiently, reducing overall system complexity.
Data Source
AI summary
An information processing apparatus according to the present disclosure includes: an acquisition unit that acquires music information; an extraction unit that extracts a plurality of types of feature amounts from the music information acquired by the acquisition unit; and a generation unit that generates information in which the plurality of types of feature amounts extracted by the extraction unit is associated with predetermined identification information as music feature information to be used as learning data in composition processing using machine learning.