Multimodal Optical Feature Extraction with a Free-Space Photonic Extreme Learning Machine

Abstract

Photonic extreme learning machines (PELMs) replace a digitally trained hidden layer by a fixed optical transformation, allowing a high dimensional feature map to be generated by physical propagation while only the final readout is learned. Existing free-space PELM demonstrations have established this principle for image and tabular benchmarks, but a unified multimodal optical feature extractor spanning structurally different data types has remained largely undeveloped. Here we demonstrate a single free-space PELM platform for image, audio derived, binary tabular, and regression tasks using phase only SLM encoding, Fourier like free space propagation, and camera intensity detection. The same optical apparatus achieves 96.56% accuracy on MNIST, 95.67% on spoken digit audio from log-Mel spectrograms, 100.00% on Mushroom classification, and 0.0699 NRMSE on Abalone regression. To our knowledge, this is the first free space PELM spanning image, audio derived, and tabular tasks in one physical pipeline, and the first PELM implementation of spectrogram based spoken digit classification. Empirical distance preservation and kernel alignment diagnostics reveal two operating regimes: geometry preserving for image and regression tasks, and distributed class mean accumulation for audio derived spectrograms. These results establish multimodal PELMs as a practical route toward general purpose optical machine learning.