AI4AV (Artificial Intelligence for Audiovisual): Design and Evaluation of a Shared System for LAMs

paper, specified "short paper"
Authorship
  1. 1. Maria Esteva

    Texas Advanced Computing Center - University of Texas, Austin

  2. 2. Tanya Clement

    Department of English - University of Texas, Austin

  3. 3. Weijia Xu

    Texas Advanced Computing Center - University of Texas, Austin

  4. 4. Aaron Choate

    Libraries - University of Texas, Austin

  5. 5. Hannah Robbins Hopkins

    Department of English - University of Texas, Austin

Work text
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Audiovisual (AV) materials are predominant historical and scientific records of our times, and their numbers are increasing exponentially in collecting institutions. Tasked with preserving and making AV materials available, libraries, archives, and museums (LAMs), need to find efficient and scalable curation solutions. Using machine learning (ML) to generate metadata is promising, but to adopt such methods information professionals must overcome a host of technological and cultural challenges. We introduce the AI4AV project in which we are conducting research around the design and evaluation of a system (currently a prototype) that uses ML to translate audio to text as well as natural language processing to classify and describe AV materials within open computing infrastructure that can be shared by multiple LAMs. This presentation describes the testbed collection, the ML and NLP methods and computing resources, and the protocol to incorporate LAMs values in the design and evaluation of the system.

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Conference Info

In review

ADHO - 2020
"carrefours / intersections"

Hosted at Carleton University, Université d'Ottawa (University of Ottawa)

Ottawa, Ontario, Canada

July 20, 2020 - July 25, 2020

475 works by 1078 authors indexed

Conference cancelled due to coronavirus. Online conference held at https://hcommons.org/groups/dh2020/. Data for this conference were initially prepared and cleaned by May Ning.

Conference website: https://dh2020.adho.org/

References: https://dh2020.adho.org/abstracts/

Series: ADHO (15)

Organizers: ADHO