A Generative System for Translating Musical Performance into Visual Form
Painting Music™ is a visual and computational method for representing the physical structure of musical performance through spatial pattern, movement, and embodied action.
Developed through over fifteen years of teaching, performance, and systems research, Painting Music™ reorganizes musical information into visual forms designed to support human memory, cognition, and real-time performance understanding.
Unlike traditional notation systems, which primarily describe abstract musical symbols, Painting Music™ focuses on the sequence of actions the performer’s body must execute over time — including finger movement, timing relationships, repetition, variation, hand coordination, and performance structure.
The video above demonstrates an early Object-Oriented Music™ (“OOM”) sketch of Beethoven’s Für Elise, generated from a software system designed to translate piano performance instructions into a coordinate-based visual structure.
Using this approach, musical performance can be explored as an integrated spatial composition rather than only as sequential notation. Repeating patterns, structural transitions, and execution pathways become visually observable in ways that are often difficult to perceive through traditional score reading alone.
Painting Music™ draws from influences in developmental cognition, generative learning, visual composition, music education, and symbolic systems design. The framework combines over fifteen years of teaching and systems development with studies in Philosophy of Science at SUNY, Artificial Intelligence and Machine Learning training through Caltech, color theory studies at the Art Students League of New York, and long-term work in generative music education through the Simply Music™ piano method.
At the center of the system is a diatonic sequence framework derived from a hand-drawn instructional chart shared with Amy Lynn Freeman by musician Glenn “Houston” Pomianek and later expanded into a broader generative teaching model for students at Mountain View Piano™.
The sequence organizes musical relationships spatially rather than sequentially, allowing harmonic movement, chord relationships, fingering structures, and performance patterns to be explored as interconnected systems.
This organizational structure is paired with a seven-point color model inspired in part by Leonardo da Vinci’s studies of color relationships and visual balance. Musical functions, harmonic movement, visual structure, and physical performance actions are mapped into a coordinated symbolic environment designed to support memory, pattern recognition, experimentation, and embodied learning.
Painting Music™, Object-Oriented Music™, SaaSi Cubes™, Draw A Song™, and related systems are built around the same underlying idea: that complex creative processes can be understood more intuitively when reorganized into bounded generative structures that allow individuals to compose, manipulate, visualize, and physically interact with symbolic patterns over time.
Rather than treating music only as abstract notation, Painting Music™ approaches musical learning as a generative process involving perception, movement, memory, experimentation, visual structure, and embodied performance.
This work was developed by Amy Lynn Freeman through her private teaching studio, Mountain View Piano™, in Mountain View, California.
