Overview
PSMs are facing the need to raise the quantity and the quality of content they create to address linear and non-linear channels. To do so, they are adopting agile technologies to accelerate the production and the management of massive amounts of data. The three pillars of these evolutions are data management, computing architectures, and machine learning. EBU groups are addressing these three pillars by providing:
- Development of open source tools;
- Development of good practices and standards;
- Dissemination.
An overview of the Group's activities and events in just a few slides: AIM 2023
Events
The call for papers for DTS2024 is open.
AIM WORKSHOP: GET THE MOST OUT OF YOUR RADIO PROGRAMS WITH AI: 12 December from 14:00 to 16:00 CET
Register here
Groups
The umbrella group that oversees the Metadata and AI domain works, maximizing knowledge sharing and collaboration. This group meets monthly.
The EBU and several Members have created and continue to develop a tool for benchmarking machine learning-based applications. After designing and publishing a tool to benchmark Speech-To-Text (STT) systems. The group is now working on facial recognition.
The main activity of this group is to support Members in the domain of metadata, which plays an essential role in media. To do so, the group develops specifications and promotes innovation, such as the use of semantic technologies.
AI and Automatic Metadata Extraction (suspended)
The group has been suspended, and the presentations are still available in the workspace. Work addressed in this group includes metadata schemas, the capabilities and performance of automatic metadata extraction tools, and the development of machine learning algorithms and related tools.
Media Cloud Microservice Architecture (MCMA)
MCMA is a serverless/micro-services strategy for media. It aims to help developers to move to "Function as a Service" architectures. Standardization of MCMA has started with SMPTE. This project provides open-source libraries.
Communities
Currently, the following deliverables are planned (green indicates the deliverable has already been delivered). Note that deliverables are dependent on enough participation in the work and that the planning is subject to change. New deliverables are added regularly.
2023
Development of datasets to evaluate facial recognition systems: the biggest for AV content!- Publication of the first release of the EBUCorePlus : the EBU ontology for media
- DataTech Seminar 2023
- ETC/SMPTE task force on AI: engineering report AI for Media
Development of the cloud-hosted AIM platform to evaluate, expose, and exchange AI applications- Development of an open-source Facial Recognition Framework for Video
- Knowledge sharing on metadata/data/AI technology
- Maintenance of the MCMA libraries and MAM
- development of the EBU hub to exchange MCMA modules with SMPTE
- Advanced Studies on Machine Learning for Members
2022
- Open source demo MAM based on serverless MCMA framework (Q2 2022)
- MDN Workshop 2022 ( Q2 2022)
- Fake News detector for English text: API open to Members (Q2 2022)
- EBUCore+ Demonstrator Kit: a cloud-hosted demo (Q3 2022)
- Cloud-hosted Metadata Exchange Platform for archive (Q3 2022)
- Knowledge Sharing on MetaData and AI (Q4 2022)
- Organisation of DataTech Seminar 2023
- Development of an open-source Facial Recognition Framework for Video (Q1 2023)
2021
- Publish a new release of STT Benchmarking (Q1 2021)
- Feasibility study of a cloud-agnostic engine to develop workflows (Q1 2021)
- Release a standardisation at SMPTE (Q4 2021)
- MDN Workshop 2021 ( Q2 2021)
- Development of an open API to expose the EBU Fake News Detector (Q2 2021)
- Open-source libraries on GitHub for AWS, Azure, GCP (Q4 2021)
- Sharing good practices, state of the art and catalogue of AME tools (Q4 2021)
- Knowledge Sharing on MetaData and AI (Q4 2021)
2020
- Google platform development and integration in front-end (Q1 2020)
- MCMA Developers Workshop @ Bloomberg (USA) (Q1 2020)
- Code factorisation of FFMPEG and STT benchmarking (Q1 2020)
- Presentation and demonstrations at EBU PTS 2019 (Q1 2020)
- Add the Levenshtein distance to the STT benchmarking code (Q1 2020)
- Test the first STT benchmarking software release with the three metrics (Q1 2020)
- Test the STT benchmarking API and Docker image (Q1 2020)
- Study on action detection and identification (Q1 2020)
- Study on fake news detection (Q1 2020)
- Publish STT benchmarking release 1.0.0 on PyPi (Q2 2020)
- Update the STT benchmarking documentation on ReadTheDocs (Q2 2020)
- EBUCore 1.10 - EBU Tech 3393 (Q2 2020)
- CCDM 2.2 - EBU Tech 3351 (Q2 2020)
- MDN Workshop 2020 (Q3 2020)
- Study on action detection and identification (Q1 2020)
- Study on fake news detection (Q1 2020)
- Development of AI models for authorship identification for written content (Q3 2020)
- Development of AI models for fake news identification for written content (Q3 2020)
- Develop the STT benchmarking new metrics for 1.1 on Github (Q4 2020)
- Open-source libraries to facilitate the development and the deployment of microservices-based architectures in serverless clouds (Q4 2020)
- Development of a POC for authorship identification (Q4 2020)
- Development of a POC integrating fake news and authorship identification (Q4 2020)
2019
- EBUCore 1.9 (Q1 2019)
- CCDM 2.1 (Q1 2019)
- IBC 2019 booth MCMA demonstration (Q3 2019)
- AWS and Azure services (speech/text processing, face recognition, and front-end) (Q3 2019)
- Update of the MCMA git with cleaned libraries and node.js (Q3 2019)
- MDN Workshop 2019 (Q3 2019)