A B C D E F G H I J K L M N O P Q R S T U V W X Y Z all

listening experience database project full details

external website icon

Champion: Enrico Motta
Professor of Knowledge Technologies Email Icon Website Icon RDF Icon

Participant(s):Alessandro Adamou, Mathieu d'Aquin, Enrico Daga, Thiviyan Thanapalasingam

Similar Projects:FindLEr

Timeline:01 Jan 2013 - 31 Mar 2018


Listening Experience Database

A crowd-sourced linked data resource of documented experiences of listening to music

The Listening Experience Database (LED) project is a collaboration between the Open University and the Royal College of Music. It has been awarded a £0.75m grant over three years from the Arts and Humanities Research Council.

The main purpose of the project is to collate people�s experiences of listening to music. It will also be used to shed light on a wide range of issues, including musical performance and reception, particularly in relation to the RCM's expertise in Western musical traditions.

The project focuses on the building of a large database of personal listening experiences, relating to any culture and repertoire up to the present. It looks at sources such as diaries, memoirs, letters and oral history.

LED is entirely managed and published as Linked Data, and reuses data from external sources (including DBpedia, the British National Bibliography and MusicBrainz) as part of its life-cycle.

  • The Open University Faculty of Arts
  • Royal College of Music


Publications | Visit External Site for Details Publications | Visit External Site for Details  

Adamou, A., Daga, E. and Isaksen, L. (eds.) (2016) WHiSe 2016 - Humanities in the Semantic Web, Workshop: 1st Workshop on Humanities in the Semantic Web (WHiSe 2016) at 13th ESWC Conference 2016, Anissaras, Greece, 1608, CEUR-WS.org

Publications | Visit External Site for Details  

Brown, S., Barlow, H., Adamou, A. and d'Aquin, M. (2015) The Listening Experience Database Project: Collating the Responses of the 'Ordinary Listener' to Prompt New Insights into Musical Experience, The International Journal of the Humanities: Annual Review, 13, pp. 17-32, CGPublisher

View By

Research Themes

Latest Seminar
Shuang Ao
Knowledge Media Institute

Building Trustworthy AI: Uncertainty Quantification and Failure Detection in Large Vision-Language Models

Watch the live webcast


Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support


If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team