The Solar Oracle Walkman

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  Sony WM-F107 exhibited in Solar Biennale 2025 in Lausanne.

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  The DIY I-V tester made by Marc Dusseiller.

Abstract

This multidisciplinary research combines DIY solar practice, conceptual music, and the exploration of AI latent space. A “solar oracle walkman” is prototyped that turns a handmade dye-sensitized solar cell (DSSC) into music. Central to the design is a 7-dimensional I–V “fingerprint”: the unique curve of each handmade DSSC is treated as a musical score, translating its photovoltaic character into reproducible, generative sound. Reinvented from a DIY I–V tester, the device is self-powered: a 6×6 cm “solar mini disc” can be inserted and rendered to audio in real time. Machine learning provides the medium for this translation. The sound engine is implemented in Max/MSP with RAVE, a neural audio synthesizer whose latent space encodes timbral variation. At present, the I–V fingerprint is manually mapped to the decoder’s latent inlets, a minimal design that prioritizes real-time responsiveness and artistic control. Beyond sculptural experimentation, the work also opens a research path: perception and AI can be viewed as two sides of the same generative mechanism, where the brain actively predicts and corrects sensory input, and hallucination is an extreme case of prediction mismatch. Yet, this project does not attempt to solve these questions now; the solar oracle walkman remains a simple art sculpture. The theories and the initial hypotheses mainly guide the design of datasets and priors for RAVE mapping and training. The project also fosters systematic collection of natural-dyed DSSC data and invites a discussion of whether solar energy could be made traceable.

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  The measurement of the I-V curve tester is uploaded to Thingspeak and a local server, and can be fetched in Max/MSP.

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  DIY DSSC with screen printed pattern and hollyhock dye made by Shih Wei Chieh.

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  DIY DSSC with cyanotype pattern made by Shih Wei Chieh.

Experiment

The audio output of each "solar disc" are expected to be reproducible, generative and variational, like a period of generative music with clear mechanism rather than completely randomness. To make each solar glass a generative device, I firstly assume I need to design a hash operation to gain a “finger print (F)” for each solar glass; A hash operation is the process of feeding input data such as numbers, text, files, or a set of I-V curve parameters—into a mathematical function or algorithm to produce a hash value. Hash algorithms can take input of any length but always generate a fixed-length output. They are designed to be fast to compute, yield the same output for the same input, and produce drastically different outputs when the input changes even slightly. Running a RAVE model on mobile or wearable hardware remains challenging. There is no suitable on-device solution yet. Jetson-nano-class devices are possible, but power consumption and big size makes them weak candidates for a wearable design. Current focus stays on data quality, dataset design, vector space preservation of sonification and the philosophical mechanism on a prototyping stage.

The finger prints raw features

As we know I-V curve is often used to analysis the characteristics of a solar cell, therefore it is ideally the finger print of the panel. In this research, the shape of I-V curve is deconstructed into to 7 features that are often used to measure different characteristics of the panel, and then apply machine learning to each feature so the shape can be learned by the computer. This method is expected to ensures the irradiance invariance, so the reproducibility of the audio output of the solar disc will be resilient even it's put under different light exposure. The finger prints F consists 7 features of the I-V curve: F = [FF (Fill Factor), Vmpp/Voc, Impp/Isc, Rs (series resistance), Rsh (shunting resistance), sum of curvature, total area of the I-V curve]. Noticing the calculation made here are dimensionless. A dimensionless feature vector is a set of numerical descriptors that have been normalized so they no longer carry physical units such as volts, amperes, or ohms. By converting raw measurements into dimensionless quantities—for example, by taking ratios like Vmpp/Voc or Impp/Isc, the features capture only the relative shape or behavior of the data, independent of its absolute scale. This process is crucial when comparing or classifying I-V curves under varying light intensities, as it ensures that differences in the vector reflect intrinsic device characteristics rather than changes in measurement conditions. The feature definitions (scale-free) are listed below, and a script of javascript is created by GPT to generate these 7 features in a Max patch:

The 7-D fingerprint is defined as: F = [FF, Vmpp/Voc, Impp/Isc, Rs*, Rsh*, Σκ, A*] All features are computed on a 64-point resampled I–V trace and normalized by Voc and Isc to be invariant to irradiance and device size.

FF (fill factor)

FF = (Vmpp * Impp) / (Voc * Isc)

Vmpp/Voc and Impp/Isc

Scale-free ratios capturing the operating point at maximum power.

Rs* and Rsh* (dimensionless ohmic estimates)

First estimate the local slopes on the resampled curve:

Rs ≈ -ΔV/ΔI (evaluated near I ≈ Isc)

Rsh ≈ -ΔV/ΔI (evaluated near V ≈ Voc)

Then report dimensionless forms:

Rs* = Rs * (Isc / Voc)

Rsh* = Rsh * (Isc / Voc)

Σκ (curvature_sum)

Sum of absolute turning angles along the 64-point polyline of the I–V trace: for each consecutive pair of segments s_i = (ΔV_i, ΔI_i), accumulate

|angle(s_i, s_{i+1})|, and report Σκ = Σ |angle(s_i, s_{i+1})|.

(Intuition: larger Σκ indicates a more “bent” I–V shape.)

A* (normalized area under the I–V curve)

Definition: area from V=0 to V=Voc divided by (Isc * Voc).

Discrete approximation on the resampled trace:

A* ≈ (Σ I[i] * ΔV[i]) / (Isc * Voc)

Ml.scale and ml.principle

To achieve the goal to recognize the shape of each "solar mini disc", ml.* library in Max/MSP could be a possible solution. Ml.* is a toolbox of machine learning algorithms implemented in Max to enable real-time interactive music and video with unsupervised machine learning, aimed at computer musicians and artists. The I-V curve tester is connected to computer and the 16 points of voltage and current measurements are sent to the computer via serial communications. The raw 7 features are first sent to ml.scale object for the normalization in range from 0 to 1. The values are then passed to ml.principle, which performs Principal Component Analysis (PCA). This converts the 7 values into a new 7-dimensional PCA space, though typically only the first two components (PC1 and PC2) carry most of the significant variance. In short, PCA is a mathematical method that rotates and compresses data into fewer dimensions while preserving as much variance as possible. ml.principle is the Max/MSP object that implements PCA: it learns the principal axes from training data, and then projects new data into that reduced space. I am not familiar with how fundamentally the mathematics works, however, I got an okay explanation from GPT below in the photo gallery.

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  An example Max patch of the machine learning process for the raw 7 features: [FF, Vmpp/Voc, Impp/Isc, Rs, Rsh, curvature_sum, area].

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  The mathematical explanation for the principle component analysis (PCA) made by GPT.

RAVE as Latent Oracle: Design Flow and Philosophical Mechanism

This work proposes a RAVE latent oracle, where the latent space of a real-time audio variational auto-encoder (RAVE) serves as an interface between physical energy and machine intelligence. In blockchain systems, oracles provide access to external data that a closed ledger cannot sense. Likewise, most AI models are closed symbolic systems without direct perception. Dye-sensitized solar cell (DSSC) I–V fingerprints are introduced as inputs and processed by RAVE, functioning as a fuzzy extractor that maps noisy physical signals into reproducible latent coordinates. These embeddings act as an oracle that grounds the model with the uniqueness of energy curves. Philosophically, perception and AI can be regarded as two sides of the same generative mechanism. Brains do not passively receive the world; they predict and correct it. Hallucination is an extreme case of prediction mismatch. In this system, the oracle has a dual role: as an anchor, it constrains generation with empirical data; as an invocation, it allows imagination to survive under verification, keeping creative variance while containing drift. Stinson’s model of generic mechanisms clarifies that both neural systems and computational models can instantiate the same kind of generative architecture. The DSSC–RAVE pipeline is not a superficial imitation but another instantiation that transforms energy inputs into perceptual outputs, illustrating how controlled hallucination becomes both a technical resource and an artistic metaphor.

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  Diagram of Stinson's generic mechanism.

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  A logical empiricist picture of a scientific theory. Reproduced from Herbert Feigl 1970.

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  Diagram of Stinson and Feigl's model applied to the current project structure.

From sound reproducibility to semantic stability

At present, the sound engine simply connects the 7-dimensional fingerprint to the RAVE decoder inlets. This minimal design already yields strong reproducibility: each solar cell produces a stable sonic identity whenever measured. Looking forward, methods from cross-modal embedding research (such as Jha et al. 2025 on vec2vec) suggest how this mapping could be formalized. According to Jha et al. (2025), In the vec2vec framework, semantic stability is maintained through three key constraints: reconstruction (the translated representation can be mapped back to the source), cycle-consistency (round-trips preserve meaning), and vector space preservation (VSP), which ensures that pairwise distances among embeddings remain intact across the mapping. Such ideas are not yet implemented, but they provide useful guidance for future experiments on how photovoltaic features might align more systematically with audio latent space.

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  A simple sound map diagram of I-V data and RAVE latent space.

Discussion

Where things are now

1. The oracle walkman works as a simple art sculpture that sonifies DSSC I–V curves in real time. The 7-feature fingerprint is stable across illumination changes after normalization.
2. The mapping is deliberately minimal, which makes evaluation of reproducibility straightforward. A controlled pipeline from sensing to sound is established in Max/MSP.
3. Perception and AI are treated as two sides of the same generative mechanism. The working definition of hallucination is generation that drifts beyond admissible evidence and priors. Brains predict and correct; hallucination is an extreme case of prediction mismatch. The oracle provides external anchors to keep generation within verifiable bounds while leaving room for creative variance.

What the theory is doing now

1. Stinson’s generic-mechanism view motivates treating DSSC–RAVE and human perception as different instantiations of a common generative architecture.
2. Feigl’s correspondence model motivates explicit bridges from observation to latent variables, so every design step is tied back to measurable traces.
3. These theoretical lenses are not goals in themselves. They function as design guidelines for dataset building, priors for mapping, and evaluation metrics for drift and variance.

Next steps

1. Build a small but clean training set of DSSC fingerprints with controlled illumination and temperature, then test monotonicity and local smoothness priors.
2. Prototype vec2vec-style constraints: simple cycle checks and distance preservation on a held-out set; log when sonic neighborhoods fail to match energy-curve neighborhoods.
3. Investigate lightweight inference targets and compression for future mobile use.
4. Explore whether traceable energy records can be registered as verifiable hashes derived from sonic fingerprints, then evaluate failure modes and anti-counterfeiting limits.

The design of the solar mini disc walkman is therefore not only a music project. It also opens a measured way to ask whether energy can be traceable and traded, and how a physical fingerprint might act as an oracle that links matter, perception, and computation without collapsing artistic variability.

References

1. Buckner, Cameron J. 2023. From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence. 1st ed. Oxford University PressNew York. https://doi.org/10.1093/oso/9780197653302.001.0001.
2. Stinson, Catherine. 2020. “From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence.” Philosophy of Science 87 (4): 590–611. https://doi.org/10.1086/709730.
3. Jha, Rishi, Collin Zhang, Vitaly Shmatikov, and John X. Morris. 2025. “Harnessing the Universal Geometry of Embeddings.” arXiv:2505.12540. Preprint, arXiv, June 25. https://doi.org/10.48550/arXiv.2505.12540.
4. https://www.hackteria.org/wiki/A_RAVE_and_starvation_synth_based_generative_sonic_device_powered_by_dye_sensitized_solar_cell
5. https://github.com/shihweichieh2023/IVcurve_tester
6. https://github.com/rjha18/vec2vec
7. https://medium.com/@shihweichieh/generative-systems-and-extended-mind-as-transformation-similarity-models-connecting-two-cultures-0352458fe85c