Dig that Lick. Analysing large-scale data for melodic patterns in jazz performances

挖那个舔。

• 批准号: 329230968
• 负责人: Professor Dr. Martin Pfleiderer
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/329230968?language=en
• 关键词: Dig; Lick.; Analysing; large; scale

The recorded legacy of jazz performance now spans a century and offers a trove of knowledge about improvisation for exploration and analysis. A thorough account of these practices involved and processes in which jazz is performed is now possible thanks to significant advances in the fields of computational analysis of audio content (including Music Information Retrieval) and information management technologies (including Big Data and Semantic Web technologies). The former enables the automatic description of audio recordings in terms of high-level musical aspects, and the latter allows such analyses to be linked to bibliographic metadata describing performers, composers, and arrangers as well as historical and social metadata describing listeners and contexts of music performance and consumption. These technologies are now reaching a level where automatic semantic metadata extraction and aggregation technologies can facilitate access to large collections in an unprecedented way, fostering new interdisciplinary research opportunities. In a close collaboration between musicologists and computer scientists we will develop the methods to create a deeper and more comprehensive understanding of jazz music and its evolution in relation to the social and cultural context.The project focuses on the development and history of melodic patterns (or "licks") in jazz improvisations. As a purposeful effort, we will complete the full cycle of methods from raw media data (audio recordings) to an understanding of the transmission and transformation of patterns, which will be aesthetically contextualised and historically situated. The project brings together renowned scholars and results from several high-profile projects on both sides of the Atlantic to assemble the technical infrastructure, create the tools, conduct the technical analysis and develop the humanistic and social scientific methods for this domain and cultural studies more generally.The project seeks to extend the research paradigms of jazz studies by:- optimising existing infrastructures for the deployment of semantic audio analyses over large collections;- investigating the benefits and barriers of state-of-the art Semantic Web technologies for information management;- facilitating access to large collections of audio and associated metadata by creating easy to use interfaces for content selection, semantic analysis, and aggregation of;- using the developed infrastructure to analyse the use of melodic patterns in a large jazz corpus; and- relating the analytic results to metadata and background knowledge representations in order to trace and interpret musical influence across time and space as well as various historical and social context.

爵士乐表演的记录遗产现在跨越了一个世纪，并提供了一个关于即兴创作的知识宝库，以供探索和分析。由于音频内容的计算分析（包括音乐信息检索）和信息管理技术（包括大数据和语义网技术）领域的重大进步，现在有可能对爵士乐所涉及的这些实践和过程进行全面的描述。前者使音频记录的自动描述方面的高层次的音乐方面，和后者允许这样的分析被链接到书目元数据描述表演者，作曲家和作曲家，以及历史和社会元数据描述听众和音乐表演和消费的背景。这些技术现在已经达到了一个水平，自动语义元数据提取和聚合技术可以以前所未有的方式促进对大型集合的访问，从而促进新的跨学科研究机会。在音乐学家和计算机科学家的密切合作下，我们将开发方法，以更深入和更全面地了解爵士乐及其与社会和文化背景的关系。该项目侧重于爵士乐即兴创作中旋律模式（或“舔”）的发展和历史。作为一项有目的的努力，我们将完成从原始媒体数据（录音）到理解模式的传输和转换的方法的完整周期，这些方法将在美学背景下和历史上进行定位。该项目汇集了来自大西洋两岸的知名学者和几个备受瞩目的项目的成果，以组装技术基础设施，创建工具，进行技术分析，并为这一领域和更广泛的文化研究开发人文和社会科学方法。该项目旨在通过以下方式扩展爵士乐研究的研究范式：-优化现有基础设施，以便在大型集合中部署语义音频分析;- 调查最先进的语义网技术用于信息管理的好处和障碍;-通过创建易于使用的内容选择、语义分析和聚合界面，促进对大量音频和相关元数据的访问;-使用已开发的基础设施来分析大型爵士乐语料库中旋律模式的使用;以及-将分析结果与元数据和背景知识表示相关联，以便追踪和解释跨越时间和空间以及各种历史和社会背景的音乐影响。

项目成果

Don’t hide in the frames: Note- and pattern-based evaluation of automated melody extraction algorithms

不要隐藏在框架中：基于音符和模式的自动旋律提取算法评估

• DOI: 10.1145/3358664.3358672
• 发表时间: 2019
• 期刊: Proceedings of the 6th International Conference on Digital Libraries for Musicology
• 作者: K. Frieler;D. Başaran;F. Höger;H.-C. Crayencour;G. Peeters
• 通讯作者: G. Peeters