Markov Model Tone Sequence Extraction for Improvisation Phrase Generation
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Solution Overview
Problem
Learners of improvisation techniques face difficulties in performing outside the rules of music theories, resulting in a lack of variety and genuine intuitive improvisation, as they rely on pre-existing collections of phrases based on music theories.
Innovation Solution
An improvisation performance analysis system that extracts tone sequence patterns from actual performances using a Markov model to calculate transition and appearance probabilities, allowing for the generation of performance phrases that reflect subconscious knowledge and intuition, independent of music theories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If learners use pre-existing collections of improvisation phrases based on music theories, then they can learn phrases with interpretations provided by musicologists and musicians, but their performance patterns lack variety and they cannot perform outside the rules of learnt music theories
Solution Approach 1:
The patent replaces the mechanical system of manually copying and practicing phrases from music theory collections with an automated computer-based system. The computer automatically analyzes music data, extracts tone sequence patterns, calculates probabilities, and generates performance phrases, eliminating the need for learners to manually copy phrases while providing more varied and intuitive improvisation patterns that go beyond conventional music theory rules.
2Reliability
If learners repeatedly listen to and learn from other performers' past improvisation performances, then they can acquire improvisation techniques, but the learning process is time-consuming and laborious
Solution Approach 1:
The patent uses computer-based copying and analysis of music data from past improvisation performances. Instead of learners repeatedly listening to and manually analyzing performances, the computer automatically copies music data, extracts tone sequence patterns, and identifies statistical characteristics, dramatically reducing the time required while maintaining or improving learning effectiveness through comprehensive data analysis.
Solution Approach 2:
The system performs preliminary analysis of music data and pre-extracts tone sequence patterns and probability calculations before learners need to use them. This preliminary processing by the computer prepares ready-to-use performance phrases that learners can directly apply, eliminating the time-consuming process of manual analysis and preparation.
3Reliability
If learners copy and practice learnt techniques from past performances, then they can acquire improvisation skills, but they cannot realize genuinely intuitive improvisation performances
Solution Approach 1:
The patent replaces the mechanical process of copying and practicing with an automated system that analyzes music data and generates performance phrases based on statistical patterns. This substitution enables learners to access intuitively-created phrases that reflect subconscious knowledge, allowing them to perform more genuinely intuitive improvisations rather than mechanically copying learned patterns.
Data Source
AI summary
The purpose of the present invention is to provide a system capable of analyzing intuitively-created improvisation performances without relying on music theories. There is provided an improvisation performance analysis system, comprising: a music information coding section 10 for analyzing and coding music data of an improvisation performer stored in a music storage medium; a tone sequence pattern extraction section 11 for extracting all of first- to n-th-order tone sequence patterns which are likely to occur as n-th Markov chains in order to perform a stochastic analysis with a Markov model using the coded music data; a pitch transition sequence extraction section 12 for obtaining a pitch transition sequence for each of the extracted tone sequence patterns; a transition probability/appearance probability calculation section 13 for using the Markov model to calculate a transition probability of each pitch transition sequence and an appearance probability of each transition sequence at each of the first- to n-th-order hierarchical levels; and an improvisation performance phrase structuring section 14 for rearranging the pitch transition probabilities at each hierarchical level based on the transition probabilities and the appearance probabilities, identifying pitch transition sequences which are statistically likely to occur and expressing the pitch transition sequences in all keys as music scores based on the twelve-tone equal temperament to thereby generate improvisation performance phrases.