Musical influence in jazz investigated with ‘Big Data’
A new project led by the Centre for Digital Music at Queen Mary University of London (QMUL) will use the latest advances in digital technology to uncover how melodic patterns have developed in jazz music.
31 March 2017
The team, which involves researchers from France, Germany, USA and City, University of London, UK, suggest that a ‘Big Data’ approach where algorithms analyse huge collections of audio samples can help to understand how musical ideas in jazz have developed as the form has grown. The team will specifically be analysing how improvised jazz solos have evolved over time.
Jazz solos hold hallowed status for fans and have been well studied by scholars, who found that improvised solos in the ‘golden age’ of jazz between 1945 and 1970 were more numerous, longer, and more dense with allusion.
Fresh insight into jazz history
This study, which hopes to amass the largest data set of jazz music, could help make comparisons between works recorded in the entire range of jazz history, not just those recorded during its golden age.
“This project aims to develop innovative technological and music-analytical methods to gain fresh insight into jazz history,” said computer scientist and jazz fan, Dr Simon Dixon, who is leading the project.
He added: “We plan to build on recent work in music information retrieval to identify the use and re-use of musical structures such as melodic phrases in audio recordings, in order to trace and interpret musical influence across time, space, cultures and societies.”
‘Dig that lick: Analysing large-scale data for melodic patterns in jazz performances’ led by QMUL is one 14 projects funded by the ESRC, the Arts and Humanities Research Council (AHRC), and 14 other international research funders, as part of the Trans-Atlantic Platform for the Social Sciences and Humanities (T-AP) Digging into Data challenge.
- Find out more about studying Sound and Music Computing MSc degree and MEng in Electronics with Music and Audio Systems at QMUL’s School of Electronic Engineering and Computer Science.
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Queen Mary University of London