2026-09-18T21:24:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1473142023-01-20T03:38:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Paradiso, Joseph A.
Wilson, Benton B.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-01-19T18:44:54Z
2023-01-19T18:44:54Z
2022-09
2022-09-16T20:24:39.896Z
https://hdl.handle.net/1721.1/147314
This thesis explores music lighting and ways in which music lighting can be generated automatically. We attempted to use videos of prior concerts as training data for a machine learning model, but ultimately this proved unsuccessful. Instead, a useful abstraction for representing, designing, and implementing light shows based on audio was designed, implemented in Python, and used to generate lighting in a few contexts. The abstraction designed in this thesis ultimately focuses on allowing developers to easily expand on the package and reuse code, with the restriction that audio data must be known ahead of time. While the current abstraction does not support live audio streams, the future work section outlines how this could be implemented.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
LightShow: Abstract Representations of Music Lighting In Python
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>This thesis explores music lighting and ways in which music lighting can be generated automatically. We attempted to use videos of prior concerts as training data for a machine learning model, but ultimately this proved unsuccessful. Instead, a useful abstraction for representing, designing, and implementing light shows based on audio was designed, implemented in Python, and used to generate lighting in a few contexts. The abstraction designed in this thesis ultimately focuses on allowing developers to easily expand on the package and reuse code, with the restriction that audio data must be known ahead of time. While the current abstraction does not support live audio streams, the future work section outlines how this could be implemented.</Abstract>
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