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On detecting the playingon-playing activity of musicians in symphonic music videos

机译:关于在交响音乐视频中检测音乐家的演奏/不演奏活动

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Information on whether a musician in a large symphonic orchestra plays her instrument at a given time stamp or not is valuable for a wide variety of applications aiming at mimicking and enriching the classical music concert experience on modern multimedia platforms. In this work, we propose a novel method for generating playingon-playing labels per musician over time by efficiently and effectively combining an automatic analysis of the video recording of a symphonic concert and human annotation. In this way, we address the inherent deficiencies of traditional audio-only approaches in the case of large ensembles, as well as those of standard human action recognition methods based on visual models. The potential of our approach is demonstrated on two representative concert videos (about 7 hours of content) using a synchronized symbolic music score as ground truth. In order to identify the open challenges and the limitations of the proposed method, we carry out a detailed investigation of how different modules of the system affect the overall performance.
机译:有关大型交响乐团中的音乐家是否在给定时间戳下弹奏乐器的信息,对于旨在模仿和丰富现代多媒体平台上的古典音乐演唱会体验的各种应用而言,都是有价值的。在这项工作中,我们提出了一种新颖的方法,可以通过有效,有效地结合对交响音乐会的视频录制的自动分析和人类注释来随时间生成每个音乐家的演奏/不演奏标签。这样,我们解决了大型合奏情况下传统纯音频方法的固有缺陷,以及基于视觉模型的标准人为动作识别方法的缺陷。在两个具有代表性的音乐会视频(大约7个小时的内容)中,使用同步的符号乐谱作为基本事实,证明了我们方法的潜力。为了确定所提出方法的挑战和局限性,我们对系统的不同模块如何影响整体性能进行了详细调查。

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