Morphing Music Generation via Time-Span Tree Data Analysis
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Solution Overview
Problem
Users with little knowledge of music find it difficult to manipulate and modify musical structures such as melody, rhythm, and harmony in existing musical composition systems, and existing techniques for morphing musical pieces are inefficient due to the complexity of calculating relative pseudo-complements.
Innovation Solution
A morphed musical piece generation system that uses time-span tree data analysis to generate intermediate musical pieces by selectively removing or adding difference information between two musical pieces, allowing users to easily create morphed pieces with adjustable influence ratios through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manipulate surface structures of music (notes, rests, chord names) using sequencers, then ease of operation is improved, but the ability to reflect user intention in deeper musical structures (melody, rhythm, harmony) deteriorates
Solution Approach 1:
The patent introduces a new dimension of musical structure manipulation by analyzing and transforming deeper structures (melody, rhythm, harmony) rather than just surface structures. This is achieved through hierarchical analysis that separates surface-level note data from deeper musical structures, enabling users to manipulate music at multiple levels of abstraction simultaneously.
Solution Approach 2:
The patent employs an intermediary computational layer that automatically analyzes musical structures and generates transformation rules. This intermediary system bridges the gap between simple user operations and complex musical structure modifications, allowing users to reflect their intentions without needing deep musical knowledge.
2Ease of operation
If the abstraction level of manipulated objects is increased to make music easier to manipulate, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The patent segments musical structure into multiple hierarchical levels: surface structures (notes, rests) and deeper structures (melody, rhythm, harmony). Each level can be manipulated independently with appropriate precision, allowing high-level abstraction for ease of operation while maintaining precision at each segment level through structured data representation.
3Adaptability or versatility
If relative pseudo-complement calculation is used for morphing musical pieces, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential morphing operation from the complex relative pseudo-complement calculation. By identifying and extracting only the necessary structural transformations needed for morphing, the system achieves adaptability without requiring full implementation of the mathematically complex original method.
Solution Approach 2:
The patent transforms the morphing process by changing parameters from complex set-theoretic operations to simpler structural transformation parameters. This allows morphing to be achieved through parameter adjustments in musical structure representation rather than through computationally intensive relative pseudo-complement calculations.
4Manufacturing precision
If manual manipulation of surface structures is required for modifying melodies, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables self-service automation where the system automatically analyzes the musical structure, identifies the deeper structures (melody, rhythm, harmony), and generates appropriate transformations. This self-service capability maintains precision while eliminating the need for manual manipulation of complex musical structures.
Data Source
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AI summary
A morphed musical piece generation system that enables even a user with little knowledge of music to easily generate a morphed musical piece between two different musical pieces is provided. A first intermediate time-span tree data generation section 6 selectively removes difference information between common time-span tree data and first time-span tree data from the first time-span tree data. Also, a second intermediate time-span tree data generation section 7 performs the same operation to obtain second intermediate time-span tree data. A data combining section combines the first intermediate time-span tree data and the second intermediate time-span tree data to generate combined time-span tree data. A musical piece data generation section generates a morphed musical piece on the basis of the combined time-span tree data.