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Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance

机译:多峰数据语料库的方法开发表达仪器音乐性能的分析

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Musical performance is a multimodal experience, for performers and listeners alike. This paper reports on a pilot study which constitutes the first step toward a comprehensive approach to the experience of music as performed. We aim at bridging the gap between qualitative and quantitative approaches, by combining methods for data collection. The purpose is to build a data corpus containing multimodal measures linked to high-level subjective observations. This will allow for a systematic inclusion of the knowledge of music professionals in an analytic framework, which synthesizes methods across established research disciplines. We outline the methods we are currently developing for the creation of a multimodal data corpus dedicated to the analysis and exploration of instrumental music performance from the perspective of embodied music cognition. This will enable the study of the multiple facets of instrumental music performance in great detail, as well as lead to the development of music creation techniques that take advantage of the cross-modal relationships and higher-level qualities emerging from the analysis of this multi-layered, multimodal corpus. The results of the pilot project suggest that qualitative analysis through stimulated recall is an efficient method for generating higher-level understandings of musical performance. Furthermore, the results indicate several directions for further development, regarding observational movement analysis, and computational analysis of coarticulation, chunking, and movement qualities in musical performance. We argue that the development of methods for combining qualitative and quantitative data are required to fully understand expressive musical performance, especially in a broader scenario in which arts, humanities, and science are increasingly entangled. The future work in the project will therefore entail an increasingly multimodal analysis, aiming to become as holistic as is music in performance.
机译:音乐表现是一种多式化体验,表演者和听众相似。本文报告了一项试点研究,该研究构成了朝着所表演经验的综合方法的第一步。我们的目标是通过结合数据收集方法来弥合定性和定量方法之间的差距。目的是建立一个包含与高级主观观测相关的多模式措施的数据语料库。这将允许系统地列入分析框架中的音乐专业人员的知识,该框架综合了既定研究学科的方法。我们概述了我们目前正在开发的方法,以创建致力于分析和探索仪器音乐性能的多模式数据语料库,从体现音乐认知的角度来看。这将使这将能够非常详细地研究仪器音乐性能的多个方面,并导致音乐创建技术的发展,从而利用从对该多种的分析中出现的跨模型关系和更高级别的质量分层,多式联版语料库。试点项目的结果表明,通过刺激召回的定性分析是一种有效的方法,用于产生音乐表现的更高级别理解。此外,结果表明了关于观察运动分析以及音乐能表现中的观察运动分析和Coarticulation,Chuncing和运动质量的计算分析的几个方向。我们认为,为满足定性和定量数据的方法的开发是完全理解表现力的音乐表现,特别是在更广泛的情景中,艺术,人文和科学越来越纠缠。因此,该项目的未来工作将需要越来越多的多语言分析,旨在变得像智能表现的音乐一样。

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