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MoDELS SIGNED

Metadata Distillery and ExpLoitation System

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

MoDELS project word cloud

Explore the words cloud of the MoDELS project. It provides you a very rough idea of what is the project "MoDELS" about.

serve recommendations effectiveness learning filters techniques innovation distillery ten searching competition grail puts decision content clients understand audio realising fuel operators automatically industry proven platform cutting interactive metadata millisecond customers reduce tv business hereby television programming data extensively cultural jobs unimaginable holy embracing original editors broadcasters players manual managed media international intelligence technological risk artificial technologies labelling rely descriptions commercialize schedule demonstrating lacks mining revenue extensive services video edge backbone time discovery models exhaustively europe global deep advertising fast heritage catalogues accurate tag

Project "MoDELS" data sheet

The following table provides information about the project.

Coordinator	MEDIA DISTILLERY NEDERLAND BV Organization address address: JOHN M. KEYNESPLEIN 12 city: AMSTERDAM postcode: 1066 EP website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Netherlands [NL]
Project website	https://www.mediadistillery.com/models/
Total cost	1˙969˙937 €
EC max contribution	1˙378˙956 € (70%)
Programme	1. H2020-EU.3. (PRIORITY 'Societal challenges) 2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs) 3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
Code Call	H2020-SMEInst-2018-2020-2
Funding Scheme	SME-2
Starting year	2018
Duration (year-month-day)	from 2018-08-01 to 2020-07-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	MEDIA DISTILLERY NEDERLAND BV MEDIA DISTILLERY NEDERLAND BV Organization address address: JOHN M. KEYNESPLEIN 12 city: AMSTERDAM postcode: 1066 EP website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	NL (AMSTERDAM)	coordinator	1˙378˙956.00

Map

Project objective

Europe's media sector is strong in creating content but lacks behind in embracing new technologies and new business models. Competition from global players that are strong at technological innovation is increasing fast. This puts many jobs, original European content and cultural heritage at risk.

The future of broadcasters is in metadata management. This data is used to fuel media applications. These will be the backbone of future television and will be used to schedule programming, interactive programming, video discovery and targeted advertising. But still today, broadcasters rely on manual labelling by editors to tag the key content of their media. The holy grail here is to do this automatically, more extensively and in real-time.

We are Media Distillery and we developed a platform that analyses every millisecond of audio and video content in real-time. We produce the most extensive deep media metadata seen in the industry. By using cutting edge deep learning, artificial intelligence and state of the art data mining techniques, our platform filters all relevant information. The current Media Distillery Platform is already well proven in providing Media Metadata services, and we already serve ten international clients.

The aim of this project is to enhance and commercialize our Metadata Distillery and ExpLoitation System (MoDELS). This enables the widespread uptake of our Deep content understanding technology and associated applications. In this project we will demonstrate its effectiveness in four media applications at European TV operators. Demonstrating that with our advanced metadata, content catalogues are better managed, content descriptions are more accurate, searching is far more exhaustively and recommendations more useful. Hereby realising business opportunities for TV operators that were unimaginable before. They understand customers better, improve their business processes, increase revenue, reduce costs and improve decision making.

Publications

List of publications.
year	authors and title	journal	last update
2018	Evangelos Stromatias, Karthik Yadati, Martin Prins, Joost de Wit Using machine learning to create personalised \'snackable\' content published pages: , ISSN: 0000-0000, DOI:	IBC Conference 2018	2019-04-18

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Send me an email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

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The information about "MODELS" are provided by the European Opendata Portal: CORDIS opendata.