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

The Solar Oracle Walkman is an exploration in energy trading and sound sculpture. The outlook design reference a retro Sony solar mini disc walkman WM-F107. The solar oracle walkman measures the I-V curve of a 6x6cm, handmade, artistically patterned DSSC with cyanotyped or screen printed TiO2 porous layer. The unique I-V curve of the solar mini disc is then uploaded to a smart contract via oracle format for verification. Once it's verified and approved, a period of generative and semantic music will be played from the walkman device based on every different variant oracle verification process. The I-V curve can be seen as the "voiceprint" of each solar mini disc and the essential components for both the anchor for energy trading and musical composition. A simple prototype is made in Max/MSP, the measured I-V curve is decomposed into seven dimensions [FF, Vmpp/Voc, Impp/Isc, Rs*, Rsh*, Σκ, A*] and then processed with Principal Component Analysis (PCA) to extract its features. The seven features are manually and a priori mapped to the latent inlets of the nn~ decoder to drive RAVE models, achieving reproducible sound and identity recognition but without semantic system built. The next verification mechanism will involve recording continuous I–V data under varying illumination and directly training a RAVE model, enabling its encoder to learn intact latent embeddings to provide better stability of cell identity. The design of the dataset is the key to achieve semantic space preservation. The interaction between the solar oracle walkman and the smart contract verification does not appear as fixed fact but emerges from the interplay between two worlds, physical energy trajectories and cognitive sound generation, the device becomes a divinatory machine linking matter, perception, and imagination.

Generative system philosophy as music composing

This chapter is expected to write about the mechanism in sound design: Perception and AI can be both understood as generative mechanisms: brains do not passively receive the world but predict and correct it. In this system, the voiceprint verification on the smart contract plays a dual role: as an anchor, constraining generation with empirical data, and as an invocation, sustaining imagination under verification.

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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.

Experiments

The solar oracle walkman is mainly made of 3 components: a I-V curve tester, a patterned solar mini disc and a smart contract. The I-V curve of each solar mini disc is measured and uploaded to a smart contract deployed on Sepolia Testnet to be verified, once its I-V data passes the verification, the corresponding music will be generated and allowed to play from the walkman accordingly.

The sound of each "solar mini disc" are expected to be reproducible, generative and semantic, like a period of generative music with clear mechanism rather than completely randomness. To make each solar mini disc a generative device, I firstly assume I need to design a hash operation to gain a “ voiceprint (V)” 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.

The extraction of voiceprints of solar mini disc

I-V curve is often used to analysis the characteristics of a solar cell, therefore it is ideally the "voiceprint" of the panel, especially the DSSC with cyanotyped and screen printded TiO2 layer. In this research, the shape of I-V curve is deconstructed into seven 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 mini disc will be resilient even it's put under different light exposure. The voiceprint V consists seven features of the I-V curve: V = [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:

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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.

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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.

The 7-D voiceprint is defined as: V = [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 seven 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 seven 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 seven 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.

From sound reproducibility to semantic stability

At present, the sound engine simply connects the 7-dimensional voiceprint to the RAVE decoder inlets. This minimal design already yields strong reproducibility and a basic cross-model way to map cell data to high fidelity audio output. However, due to the absence of vector space preservation (VSP) in this mapping, the 7-D voiceprint remains primarily a registry of unique identities, sufficient for reproducibility but unable to guarantee meaningful relational structure among different cells. This means that an oracle built on the current pipeline can reliably answer whether a signal is genuine or not, but cannot provide further semantic interpretation of how one energy curve relates to another. According to Jha et al. (2025), 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. Although the semantic stability in the current system is not yet implemented, the latent embedding of RAVE could provide a solution.

RAVE as latent oracle

This work therefore proposes a RAVE latent oracle, where the latent space of a variational auto-encoder becomes an interface between physical energy and machine intelligence. In blockchain, oracles provide trusted access to external signals; likewise, most AI models are closed symbolic systems without direct grounding. Here, DSSC I–V curves serve as fuzzy extractors, transforming noisy physical outputs into reproducible latent coordinates that act as oracle values.

I plan to collect sequential I–V data under varying illumination to build a dataset that captures the dynamic behavior of each DSSC module. This dataset will be used in Google Colab to train a RAVE model, where the encoder will learn compact latent embeddings of the cell’s non-linear characteristics. Subsequent measurements from the same DSSC will then be mapped into stable, reproducible embeddings, which will support a dual-path strategy: one directed to the decoder in Max/MSP for immediate auditory rendering, and the other transmitted to an oracle endpoint for statistical verification and on-chain submission.

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  This figure contrasts the current pipeline with an idealized design that incorporates vector space preservation (VSP). On the left, reproducibility is achieved: each DSSC maps to a stable position in latent space, allowing identity verification but without meaningful relationships across cells. On the right, VSP ensures that pairwise distances in the latent space reflect differences in photovoltaic features, providing not only reproducibility but also relational meaning. In this view, the oracle evolves from a gatekeeper that validates authenticity into a “divinatory machine” that reveals how energy curves relate within a shared semantic structure.

The sonification of hallucinations

An interesting and simple strategy is to foreground hallucination as a sonic principle, I isolate what the model fails to explain. After training a RAVE on DSSC I–V sequences, each new measurement produces a latent embedding z1. Passing this through the encoder–decoder loop yields a reconstructed embedding z2. The residual vector r = z1 – z2 is then extracted: this residual represents precisely the component that lies outside the model’s learned prediction. While z1 captures the reproducible voiceprint of the cell, r embodies the “hallucination”—the deviation, noise, or anomaly that the model cannot assimilate. By routing only this residual into a separate RAVE decoder, the auditory output becomes a direct rendering of prediction mismatch. In this way, the system does not merely sonify energy curves, but also transforms epistemic failure into sound: hallucination is heard not as error, but as a generative surplus that destabilizes the boundary between perception and imagination.

  [IV curve]
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  Encoder → z1
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  Decoder → z2
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  Residual r = z1 - z2
       │
       ▼
  Decoder2 → Sound of Hallucination

Discussion

Where things are now The oracle walkman works as a simple art sculpture that sonifies DSSC I–V curves in real time. The 7-feature voiceprint is stable across illumination changes after normalization. The mapping is deliberately minimal, which makes evaluation of reproducibility straightforward. A controlled pipeline from sensing to sound is established in Max/MSP. 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 Stinson’s generic-mechanism view motivates treating DSSC–RAVE and human perception as different instantiations of a common generative architecture. Feigl’s correspondence model motivates explicit bridges from observation to latent variables, so every design step is tied back to measurable traces. 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. Current limitations highlight the absence of vector space preservation (VSP). Without VSP, the latent space serves as a stable registry of identities but cannot guarantee relational meaning across cells. Thus, the oracle functions mainly as a gatekeeper that validates authenticity but offers little semantic interpretation. With VSP, however, the oracle could evolve into a “true oracle machine”: not only verifying truth but also revealing how different energy curves relate, translating physical differences into interpretable structures of another domain. Next steps Build a small but clean training set of DSSC voiceprints with controlled illumination and temperature, then test monotonicity and local smoothness priors. 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. Investigate lightweight inference targets and compression for future mobile use. Explore whether traceable energy records can be registered as verifiable hashes derived from sonic voiceprint, 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 voiceprint might act as an oracle that links matter, perception, and computation. With stronger constraints such as VSP, this oracle could transcend its role as a verifier and begin to function as a “machine of divination,” translating one world into another 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