Posté par: kevinjblade Il y a 2 semaines, 5 jours
Escencionalismo is an ongoing artistic and technological investigation into the possibility of creating a form of musical behavior that emerges from the interaction between living systems, computational models, and adaptive learning structures.
The project began with a simple question:
Can biological activity become a compositional force rather than merely a source of control data?
Many plant-sonification systems transform electrical fluctuations into direct mappings: pitch, MIDI notes, filter movement, or procedural triggers. Escencionalismo instead attempts to move away from fixed mappings and toward the development of behavioral relationships.
The current prototype uses an Arduino-based sensing system connected to plants through conductive electrodes. Signals are read through multiple channels and streamed into Pure Data. Rather than using these values as deterministic controllers, they are interpreted as evolving states.
Machine learning enters as a mediating layer.
Using FluCoMa within Pure Data, sensor relationships can be collected into datasets and used to train predictive systems. The goal is not classification in a conventional sense, but the development of a structure capable of identifying tendencies, recurring patterns, and emergent behaviors.
The idea is closer to memory and adaptation than simple automation.
Recent experimentation has focused on multi-sensor arrangements and the possibility of modeling relationships among several biological inputs simultaneously. During development, phenomena resembling signal coupling and shared fluctuations emerged unexpectedly. Instead of treating this as merely a technical problem, these observations suggested conceptual parallels with synchronization theory.
During the IRCAM Forum, I encountered references to the Kuramoto model, which immediately resonated with the project's direction.
Kuramoto's framework describes how independent oscillators can spontaneously synchronize through interaction. The implications are compelling for Escencionalismo: instead of designing a system around isolated sensor channels, one can imagine a network of interacting entities whose relationships become the compositional material itself.
In this perspective, plants, sensors, machine learning structures, and musical agents become oscillatory participants within a dynamic ecosystem.
Rather than asking:
"What note should a plant produce?"
the question becomes:
"How do independent entities gradually negotiate coherence?"
This shift transforms sonification into an investigation of emergence.
The technical ecosystem currently includes:
– Arduino sensor acquisition
– Pure Data
– FluCoMa machine learning objects
– Adaptive dataset generation
– Multi-channel biological sensing
– Explorations of synchronization models
– Future audiovisual and spatial extensions
Escencionalismo remains in an exploratory stage, and the project intentionally embraces uncertainty. Unexpected correlations, noisy data, instability, and imperfect biological measurements are not considered failures; they become part of the aesthetic and conceptual material.
The objective is not to extract music from nature.
It is to construct conditions where musical behavior can emerge between systems.
I welcome discussion from artists, researchers, and developers interested in machine listening, emergent systems, biological interfaces, and AI-assisted creative practices.
DEMO:
https://vimeo.com/1138144739?share=copy
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