Design for Virtuosity: Modelling and Supporting Expertise in Digital Musical Interaction

音乐表演者要花很多年才能熟练掌握他们的乐器。新创造的数字乐器(DMIS)面临着采用的重大障碍，因为很少有表演者愿意重复这些年的培训，以培养对未知乐器的专业知识。没有专业玩家，评估DMI设计的成功是具有挑战性的，在更广泛的音乐社区建立它的地位几乎是不可能的。因此，尽管在过去的十年里创造了许多数字乐器，但除了最初的几次表演之外，很少有数字乐器能产生持久的影响。该奖学金提出了一种新的DMI设计方法，重新利用训练有素的音乐家的现有技能和经验，为他们提供了一条快速掌握技艺的途径，而无需多年的再培训。这项研究计划围绕两个相辅相成的主题：研究演奏者与乐器的互动，以及创造新的乐器，充分捕捉高超演奏的丰富性和微妙。首先，将开发演奏者与乐器之间的互动模型。乐器演奏可以被认为是人机交互的一种特殊情况，这既因为它的复杂性，也因为乐器成为身体的延伸这一普遍经验：当演奏时，表演者往往没有意识到乐器。受控实验和参与式设计练习将确定仪器的设计如何影响专业知识的发展，以及如何将现有的专业知识转移到新创建的仪器中。第二，生成的模型将应用于DMI创建过程，采取统一硬件设计、数字信号处理、人机交互(HCI)和艺术考虑的整体方法。现有的DMIS经常含蓄地优先考虑计算机的便利性，而不是人类玩家的体验。在声学乐器上，整个物理物体对声音的贡献是多么微妙，但DMI中传感器的选择通常会将表演者的动作减少到机器可以处理的几个维度。该奖学金将创建故意对相互作用进行过采样的仪器，使用更多的传感器和更高的采样率，而不是为了创建更复杂的仪器，而是为了捕捉专家们珍视的细微差别。对新的DMIS的评估将有助于改进最初的表演者-乐器交互模型。该项目侧重于音乐交互，但专业知识重新用途的原则在人机界面中广泛适用。人类用户的能力不能仅仅通过通用的认知和运动过程来建模；真人拥有经过多年实践发展起来的专业技能，与这些技能相关的新技术比必须从头开始学习的技术更有可能被接受。音乐是一个很好的测试案例，因为器乐培训是广泛的和合理的标准化，但研究结果将与其他专业领域相关。这种奖学金支持了PI和两名博士后研究人员的时间。一名博士后将专注于表演者的研究和互动，另一名将专注于数字信号处理和数据映射策略。在整个研究过程中，与音乐家的密切合作将确保其与该社区的相关性。本研究集硬件设计、数字信号处理、人机交互、认知科学、音乐学和艺术实践于一体。PI拥有音乐创作、电子工程和人机交互方面的背景和专业活动，是领导这个多学科项目的理想人选。

Musical performers spend many years achieving proficiency on their instruments. Newly-created digital musical instruments (DMIs) face a significant barrier to adoption in that few performers are willing to repeat these years of training to develop expertise on an unknown instrument. Without expert players, evaluating the success of a DMI design is challenging, and establishing its place in a broader musical community is nearly impossible. As a result, while many digital instruments have been created over the past decade, few have achieved lasting impact beyond the first few performances.This fellowship proposes a new approach to DMI design which repurposes the existing skills and experience of trained musicians, providing them with a rapid path to virtuosity without years of retraining. The research programme is organised around two complementary themes: study of performer-instrument interaction and creation of new instruments which capture the full richness and subtlety of virtuosic performance.First, models will be developed of the interaction between performer and instrument. Instrumental performance can be considered a special case of human-machine interaction which is interesting both for its complexity and for the common experience that the musical instrument becomes an extension of the body: while playing, the performer is often not consciously thinking about the instrument. Controlled experiments and participatory design exercises will establish how an instrument's design affects the development of expertise, and how existing expertise can be transferred to newly-created instruments.Second, the resulting models will be applied to the DMI creation process, taking a holistic approach unifying hardware design, digital signal processing, human-computer interaction (HCI) and artistic considerations. Existing DMIs often implicitly prioritise the convenience of the computer over the experience of the human player. On acoustic instruments, the entire physical object contributes to the sound, however subtly, but the choice of sensors in a DMI typically reduces the performer's actions to just a few machine-tractable dimensions. This fellowship will create instruments which deliberately oversample the interaction, using more sensors and higher sampling rates than apparently necessary, not to create a more complicated instrument, but rather to capture the subtle nuances that experts prize. Evaluation of the new DMIs will help refine the original models of performer-instrument interaction.This project focuses on musical interaction, but the principle of repurposing expertise is widely applicable within HCI. The capabilities of human users cannot be modelled only through generic cognitive and motor processes; real people have specialist skills developed over years of practice, and new technologies which connect to those skills are far more likely to find acceptance than ones which must be learned from scratch. Music is a good test case since instrumental training is widespread and reasonably standardised, but the findings will be relevant to other expert domains.This fellowship supports the time of the PI and two postdoctoral researchers. One postdoc will focus on performer studies and interaction, the other on digital signal processing and data mapping strategies. Close collaboration with musicians throughout the research will ensure its relevance to that community. This research integrates hardware design, digital signal processing, human-computer interaction, cognitive science, musicology and arts practice. The PI, with background and professional activities in music composition, electronic engineering and HCI, is ideally placed to lead this multidisciplinary project.