一个多模态语料库,用于技术增强的小提琴演奏学习

G. Volpe, Ksenia Kolykhalova, Erica Volta, Simone Ghisio, G. Waddell, Paolo Alborno, Stefano Piana, C. Canepa, Rafael Ramírez-Meléndez
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引用次数: 12

摘要

学习演奏乐器是一项艰巨的任务,主要基于师徒模式。技术很少使用,通常仅限于音频和视频的录制和播放。然而,多模式互动系统可以补充实际的学习和教学实践,通过在自学过程中为学生提供指导,并帮助教师和学生专注于从通常的视听记录中难以理解的细节。本文介绍了由四位专业小提琴演奏家提供的成功专家模型录音组成的多模态语料库。该语料库在repoVizz平台上公开提供,包括同步音频、视频、动作捕捉和生理(EMG)数据。它代表了EU-H2020-ICT项目TELMI的参考档案,TELMI是一个国际研究项目,研究我们如何从教学和科学的角度学习乐器,以及如何开发新的互动、辅助、自学、增强反馈和社会意识系统,以支持乐器的学习和教学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multimodal corpus for technology-enhanced learning of violin playing
Learning to play a musical instrument is a difficult task, mostly based on the master-apprentice model. Technologies are rarely employed and are usually restricted to audio and video recording and playback. Nevertheless, multimodal interactive systems can complement actual learning and teaching practice, by offering students guidance during self-study and by helping teachers and students to focus on details that would be otherwise difficult to appreciate from usual audiovisual recordings. This paper introduces a multimodal corpus consisting of the recordings of expert models of success, provided by four professional violin performers. The corpus is publicly available on the repoVizz platform, and includes synchronized audio, video, motion capture, and physiological (EMG) data. It represents the reference archive for the EU-H2020-ICT Project TELMI, an international research project investigating how we learn musical instruments from a pedagogical and scientific perspective and how to develop new interactive, assistive, self-learning, augmented-feedback, and social-aware systems to support musical instrument learning and teaching.
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