Abstract: A method for identifying, recording, encoding, or reproducing electromagnetic radiation employs machine learning models. For example, each of multiple training illuminations may be measured using first and second types of systems. The first type of system may be a low-cost system that includes light sensors and measures an array of readings; the second type of system may be a precision spectrometer that measures the spectral power distribution. A trained machine learning model may predict the spectral power distribution of illumination from the array associated with the first type of system and may predict the representation of illumination understood by a spectrally tunable luminaire. The array of readings associated with the first type of system may be used to identify, record, encode, or reproduce electromagnetic radiation.

Abstract: A multi-track illumination content editing system allows creation or alteration of content using multiple input tracks representing illumination or other electromagnetic emissions and auxiliary content representing presentations other than electronic emissions, e.g., images, audio, or video. The editing system may particularly perform mixed media editing that manipulates both illumination and auxiliary content so that output illumination or auxiliary tracks depend on both illumination and auxiliary input tracks.

Abstract: A multi-track illumination content editing system allows creation or alteration of content using multiple input tracks representing illumination or other electromagnetic emissions and auxiliary content representing presentations other than electronic emissions, e.g., images, audio, or video. The editing system may particularly perform mixed media editing that manipulates both illumination and auxiliary content so that output illumination or auxiliary tracks depend on both illumination and auxiliary input tracks.