ARGIRA Unified Acoustic Dataset – 74 Images in the 3D Space A = f(H, S, I)

ARGIRA Unified Acoustic Dataset – 74 Images in the 3D Space A = f(H, S, I) This dataset combines three controlled image collections into a single acoustic feature space: 21 images from the spatial irregularity gradient (I00–I20), 10 from the synthetic benchmark v2, and 43 real paintings from Western art history. Total: 74 images. All images have been processed with the ARGIRA pipeline 07 (bandlimited sawtooth, harmonic summation up to Nyquist), extracting:- hue_std (H) – chromatic dispersion- mean saturation (S)- spatial irregularity (I) – mean absolute gradient- emergent harmonic count (A) – spectral peaks above -40 dB- fractal dimension – log-log spectral slope (500-2000 Hz) Main findings: 1. Spatial chaos ≠ statistical chaos: increasing I decreases hue histogram entropy. 2. Saturation is the dominant predictor of harmonic richness: images 04 vs 05 in the benchmark share hue_std=0.5 but opposite A (6 vs 13 harmonics). 3. Critical threshold I14: harmonic count collapses from >80 to 21 at I14 (sigma=0.7, irregularity=0.01049), stabilizing at ≈12 harmonics beyond I15. The dataset validates the model A = f(H, S, I) on synthetic and real images, demonstrating that harmonics are the critical mechanism linking visual structure to acoustic complexity. Included files: - all_images_acoustic_clean.csv (74 rows, 7 columns)- argira_3d_space_matplotlib.png (3D field visualization)- procesar_todo.py (full pipeline script)- matplotlib_3d_space.py (3D plotting script)- README.md (this text) Related deposits: - Gradient Experiment v1 (images): DOI pending- Synthetic Benchmark v2: 10.5281/zenodo.20385782- Pipelines 10–13: 10.5281/zenodo.20366726 License: CC BY-NC 4.0