Was that me?
Was that me?
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那是我吗？
DOI：
10.1145/3411109.3411137
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Brown D
中科院分区：
文献类型：
--
作者：
Brown D
传统的西方乐器已经发展成为强大和可预测的，以相同的音乐响应一致地响应相同的玩家动作。因此，在表演场景中出现的错误通常归因于表演者，因此音乐成就的标志是完美的音乐再现。数字乐器通常会增加由于乐器的一个或多个组件内的技术故障而导致的第二类错误的可能性。使用机器学习的手势乐器特别容易受到这些类型的错误的影响，因为识别准确率通常低于100%，这使得错误成为手势音乐表演的常见特征。在本文中，我们将这些与技术相关的错误称为系统错误，玩家和观众很难从表演者错误中区分出来。我们进行了一项试点研究，在该研究中，参与者在模拟系统错误的存在下重复一个音符选择任务。研究结果表明，对于所研究的手势音乐系统，控制系统错误的增加对应于表演者错误的发生和严重程度的增加。此外，我们发现系统误差降低了表演者的控制感，并导致仪器被认为是不准确和反应迟钝。
Traditional Western musical instruments have evolved to be robust and predictable, responding consistently to the same player actions with the same musical response. Consequently, errors occurring in a performance scenario are typically attributed to the performer and thus a hallmark of musical accomplishment is a flawless musical rendition. Digital musical instruments often increase the potential for a second type of error as a result of technological failure within one or more components of the instrument. Gestural instruments using machine learning can be particularly susceptible to these types of error as recognition accuracy often falls short of 100%, making errors a familiar feature of gestural music performances. In this paper we refer to these technology-related errors assystem errors, which can be difficult for players and audiences to disambiguate fromperformer errors. We conduct a pilot study in which participants repeat a note selection task in the presence of simulated system errors. The results suggest that, for the gestural music system under study, controlled increases in system error correspond to an increase in the occurrence and severity of performer error. Furthermore, we find the system errors reduce a performer's sense of control and result in the instrument being perceived as less accurate and less responsive.