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 his- torical 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 the 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.
Large collections of digitised art and music o↵er new ways to study how creativity grows and changes across time and borders. But providing ready access to these data collections, the quantitative analysis of cultural media, and the interpretation of analytic results by humanities and social science scholars still present significant methodological challenges. Dig that Lick will address these issues by creating usable tools to explore large samples of jazz audio recordings, supplemented by rich metadata on their creators and cultural context and linked to external background knowledge, to show how the transmission and mutation of elements of improvised musical language, such as musical motifs or patterns, can be understood within its aesthetic, social and historical context. It builds on current work in projects on both sides of the Atlantic to assemble the necessary technical infrastructure, create tools, conduct the analysis and frame the historical and social questions that can shed light on musical communication and knowledge production in jazz improvisation.
Jazz was first heard among the African diaspora in the New World but quickly became global and astonishingly diverse. Its study requires insights from 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 many disciplines interested in the evolution of musical expression.
The specific goals of Dig that Lick are: to enhance infrastructures for semantic audio analyses of large collections; to facilitate access to large collections of audio and associated metadata via interfaces for content selection, semantic analysis, and aggregation of results that humanities researchers can easily use; to develop this infrastructure to analyse melodic patterns across large corpora of jazz audio; and to relate the results to metadata and background knowledge in order to trace and interpret musical influence across time and space as well as cultures and societies. This will be the first time that robust, well structured discographic metadata are linked with the sonic resources they describe on a large scale. In addition the data will be linked automatically to external linked data resources such as DBpedia and MusicBrainz.
The following multidisciplinary team of Principal Investigators will lead the project: Simon Dixon, Queen Mary University of London (QMUL), United Kingdom, music informatics and computer science; Krin Gabbard: Columbia University (CU), United States of America, jazz studies, cinema, and critical race studies; Hélène Papadopoulos: National Center for Scientific Research (CNRS), France, music theory, statistical relational learning, music information re- trieval; Martin Pfleiderer: University of Music Franz Liszt, Weimar (UMW), Germany, jazz research, popular music research, sociology and psychology of music; Gabriel Solis: University of Illinois Champaign Urbana (UIUC), United States of America, jazz history, music theory, ethnomusicology; Tillman Weyde: City University London (CITY), United Kingdom, computer science, computational musicology, music information retrieval.
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