Analyze chord voicings and progressions with K-Means clustering and spectral analysis.
npx skills add https://github.com/GuitarAlchemist/ix --skill music-ml-demo
Please help me install this Agent Skill. Skill: music-ml-demo Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/music-ml-demo Command: npx skills add https://github.com/GuitarAlchemist/ix --skill music-ml-demo
This Skill addresses the complex needs of music theory analysis and algorithmic music processing, providing tools for chord clustering, harmonic complexity analysis, and scale recommendation based on mood classification.
Use the music-ml-demo skill to analyze the harmonic structure of a given chord progression and recommend a scale for soloing over it, given the target mood as "mysterious".
High-intent search queries and answers about installing and using this skill.
You can perform harmonic analysis using K-Means clustering to group chord voicings by similar harmonic structures regardless of pitch. Spectral and chaos analysis further measure predictability and complexity within chord progressions.
The best way to recommend a scale for soloing over a target mood like mysterious is using supervised learning. It classifies scales based on mood and matches them to the analyzed harmonic structure of the chord progression.
Yes, you need the GA MCP to interact with music theory tools and the ix MCP to run machine learning algorithms. These dependencies are required to execute chord clustering and spectral analysis.
Yes, you can measure harmonic complexity in a chord progression using spectral analysis and chaos analysis. These machine learning techniques quantify predictability and complexity within harmonic structures.
K-Means clustering for chord voicings groups chords with similar harmonic structures together regardless of pitch. This reveals how different voicings share underlying harmonic properties across a progression.
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