The High Performance Sound Technologies for Access and Scholarship project has been ongoing from 2012. This page includes links to projects from 2012:
In 2012, the School of Information at the University of Texas at Austin and the Illinois Informatics Institute at the University of Illinois at Urbana-Champaign received an NEH Institutes in Advanced Technologies in the Digital Humanities grant to host two rounds of an Institute on High Performance Sound Technologies for Access and Scholarship (HiPSTAS) in May 2013 and May 2014.
More information: http://hipstas.org/institute/
In 2014, the HiPSTAS Project received a second round of funding from the NEH, from the Preservation and Access Division’s Research and Development grant for HRDR (HiPSTAS Research and Development with Repositories).
More information: http://hipstas.org/hrdr/
In 2019, the AudiAnnotate Project was awarded a 2019 Digital Extension Grant from the American Council of Learned Societies to build on existing efforts by the HiPSTAS project and Brumfield Labs to produce and share (1) a web application to help scholars to create IIIF-AV annotations; (2) documentation that describes three different use cases that leverage this workflow in scholarship on poetry performance recordings; and (3) a workshop for sharing this work. The developed application and workflows will help users to translate their own analyses of audio recordings into media annotations that will be publishable as easy-to-maintain, static, W3C Web Annotations associated with IIIF manifests and hosted in a GitHub repository that are viewable through presentation software. Local, national, and international partners include the Harry Ransom Center, the IIIF Consortium, and the SpokenWeb project.
More information: http://hipstas.org/audiannotate/
In 2019, As part of Good Systems, a UT Bridging Barriers Grand Challenge, “AI.4.AV: Building and Testing Machine Learning Methods for Metadata Generation in Audiovisual Collections” is developing a methodology and workflow for libraries, archives, and museums (LAMs) to use machine learning and supercomputing resources to generate metadata for AV materials in the humanities. In the process, the project will address research questions around defining and evaluating a “good” system for introducing AI for AV to information professionals.
More information: http://hipstas.org/ai4av/