History of Recorded Jazz: DTL1000, 1920-2020

Dixon, Simon and Crayencour, Hélène and Velichkina, Olga and Frieler, Klaus and Höger, Frank and Pfleiderer, Martin and Henry, Lucas and Solis, Gabriel and Wolff, Daniel and Weyde, Tillman and Proutskova, Polina and Peeters, Geoffroy (2021). History of Recorded Jazz: DTL1000, 1920-2020. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-854781

The recorded legacy of jazz spans a century and provides a vast corpus of data documenting its development. Recent advances in digital signal processing and data analysis technologies enable automatic recognition of musical structures and their linkage through metadata to historical and social context. Automatic metadata extraction and aggregation give unprecedented access to large collections, fostering new interdisciplinary research opportunities.

This project aims to develop innovative technological and music-analytical methods to gain fresh insight into jazz history by bringing together renowned scholars and results from several high-profile projects. Musicologists and computer scientists will together create a deeper and more comprehensive understanding of jazz in its social and cultural context. We exemplify our methods via a full cycle of analysis of melodic patterns, or "licks", from audio recordings to an aesthetically contextualised and historically situated understanding.

Data description (abstract)

We present the DTL1000 dataset, which was created in the “Dig That Lick” project and covers the history of recorded jazz with a sample of 1,750 improvisations extracted from 1,060 audio tracks. The dataset contains a mixture of collected (editorial metadata), manually annotated (structure, style), and automatically generated (main melody transcriptions of solos) data describing the recordings. The motivation for creating this dataset was the study of patterns in jazz improvisation, but there are many other applications for this resource. The accompanying paper presents the dataset creation process, data structure and contents with descriptive statistics and discusses the origin and process of the annotations, as well as general use cases and specifically the case of pattern analysis. These components and their combinations enable a number of use cases for jazz studies as well as algorithm development for music analysis. The DTL1000 dataset provides a rich resource for a variety of disciplines, and constitutes a contribution to a field where large datasets with rich annotations are scarce.

Data creators:

Creator Name	Affiliation	ORCID (as URL)
Dixon Simon	Queen Mary University of London	http://orcid.org/0000-0002-6098-481X
Crayencour Hélène	National Center for Scientific Research
Velichkina Olga	National Center for Scientific Research
Frieler Klaus	Music University Franz Liszt Weimar
Höger Frank	Music University Franz Liszt Weimar
Pfleiderer Martin	Music University Franz Liszt Weimar
Henry Lucas	University of Illinois at Urbana-Champaign
Solis Gabriel	University of Illinois at Urbana-Champaign
Wolff Daniel	City, University of London
Weyde Tillman	City, University of London
Proutskova Polina	Queen Mary University of London
Peeters Geoffroy	Telecom Paris, IP-Paris

Sponsors:

ESRC (UK), NEH (USA), DFG (Germany), ANR (France)

Grant reference:

ES/R004005/1

Topic classification:

Science and technology
Society and culture

Keywords:

JAZZ, MUSIC

Project title:

Dig that lick: Analysing large-scale data for melodic patterns in jazz performances

Grant holders:

Simon Dixon, Tillman Weyde, Gabriel Solis, Martin Pfleiderer, Helene-Camille Crayencour

Project dates:

From	To
1 October 2017	30 September 2019

Date published:

06 Jul 2021 12:50

Last modified:

06 Jul 2021 12:51

Coverage and Methodology

Temporal coverage:

From	To
1 January 1920	1 January 2020

Collection period:

Date from:	Date to:
1 October 2017	30 September 2019

Country:

United Kingdom

Data collection method:

The study of jazz requires insights from, and feeds knowledge back into, African American Studies, Anthropology, Art History, Literary Studies, Music, Philosophy, Political Science, and Sociology. A thorough analysis of a century's worth of jazz recordings, and the practices the music entails, is now possible thanks to recent advances in the computational analysis of audio content, or Music Information Retrieval (MIR), and to progress in processing large datasets and information management with Semantic Web technologies. The former enables the automatic description of audio recordings in terms of high-level or structural musical aspects, and the latter allows such analyses to be linked to discographic metadata, distributed over multiple sites, describing performers and composers, listeners, performance venues, and production and consumption factors, and general historic, cultural and geographic information from external resources. These technologies can now facilitate access to large collections by researchers from the many disciplines interested in the evolution of musical expression.

Observation unit:

Individual

Kind of data:

Audio

Type of data:

Historical data, Qualitative and mixed methods data, Time series data

Resource language:

English

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