How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Mot. Imag. J
Volume: 128, No. 1, pp. 33–40
Abstract: For almost 30 years, digital nonlinear editing systems (DNLEs) have functioned as a practical replacement for film and videotape editing. While DNLEs have progressed in their capabilities by offering increased video resolutions and visual effects, they have not fundamentally changed their operational constructs. Editors must still choose in and out points of shots and then methodically edit those shots into a cohesive sequencing. Technology improvements in artificial intelligence and machine learning have the potential to profoundly impact how DNLE systems operate, and, in turn, content creation methodologies will dramatically change. This paper examines the effects of image recognition, natural speech processing, language recognition, cognitive metadata extraction, tonal analysis, and data and statistical integration on the creation of content.
Publication Date: 2019-01-01
DOI: 10.5594/JMI.2018.2876781
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2018.2876781
Author(s): Tom Ohanian
Keyword(s): Artificial Intelligence, Cognitive Metadata, Digital Nonlinear Editing, Language Recognition, Machine Learning, Speech Analysis
Copyright: © 2018 SMPTE

Bibliographic Reference(s)

M. Leake, A. Davis, A.Truong, and M. Agrawala, “Computational Video Editing for Dialog-Driven Scenes,” ACM Trans. Graph., 36(4):130, July 2017. EXTERNAL
J. Priestly, “Wimbledon to Use IBM’s Watson for Highlights, Analytics,” TV Technology, July 2017. EXTERNAL
I. Arev, H. S. Park, Y. Sheikh, J. Hodgins, and A. Shamir, “Automatic Editing of Footage from Multiple Social Cameras,” ACM Trans. Graph., 33(4):1–11, July 2014. EXTERNAL
H. Zen, K. Tokuda, and A. W. Black, “Statistical Parametric Speech Synthesis,” Speech Comm., 51(11):1039–1064, Nov. 2009. EXTERNAL
S. Suwajanakorn and S. M. Seitz, “Synthesizing Obama: Learning Lip Sync from Audio,” ACM Trans. Graph., 36(4):95, July 2017. EXTERNAL

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Tom Ohanian; How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 1, 2019); SMPTE, 2019. Available at https://doi.org/10.5594/JMI.2018.2876781

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Tom Ohanian; How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 1, 2019); SMPTE, 2019. Available at https://doi.org/10.5594/JMI.2018.2876781

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<span class="citation">Tom Ohanian; <cite>How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies</cite>, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 1, 2019); SMPTE, 2019. Available at <a href="https://doi.org/10.5594/JMI.2018.2876781" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2018.2876781</a></span>

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Tom Ohanian; How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 1, 2019); SMPTE, 2019
doi: 10.5594/JMI.2018.2876781
url: https://doi.org/10.5594/JMI.2018.2876781

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<li>
Tom Ohanian; <cite id="bib-10-5594-jmi-2018-2876781">How Artificial Intelligence and Machine Learning May Eventually Change Content Creation Methodologies</cite>, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 1, 2019); SMPTE, 2019
<span class="doi">10.5594/JMI.2018.2876781</span>
</li>