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

音乐表演者花了很多年的时间来熟练掌握他们的乐器。新创造的数字乐器（DMI）面临着一个重大的障碍，因为很少有表演者愿意重复这些年的培训，以发展一种未知乐器的专业知识。如果没有专业的演奏者，评估一个音乐设计的成功是具有挑战性的，在更广泛的音乐界建立自己的地位几乎是不可能的。因此，尽管在过去的十年里，许多数字乐器被创造出来，但很少有乐器能在最初的几次演奏之后产生持久的影响。该奖学金提出了一种新的乐器设计方法，重新利用受过训练的音乐家的现有技能和经验，为他们提供一条快速通往精湛技艺的道路，而无需多年的再培训。研究计划主要围绕两个相辅相成的主题：研究演奏者与乐器的互动，以及创作新乐器，以充分捕捉艺术家演奏的丰富性和微妙性。乐器演奏可以被认为是人机交互的一个特例，这一点很有趣，既因为它的复杂性，也因为乐器成为身体的延伸的共同体验：在演奏时，演奏者通常不会有意识地思考乐器。控制实验和参与式设计练习将确定乐器的设计如何影响专业知识的发展，以及现有的专业知识如何转移到新创建的乐器中。其次，所产生的模型将应用于乐器创建过程，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通常隐含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，整个物理对象都对声音做出了贡献，无论多么微妙，但是在乐器中选择传感器通常会将演奏者的动作减少到几个机器可处理的维度。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，而不是创建一个更复杂的仪器，而是为了捕捉专家所珍视的细微差别。对新的DMIs的评估将有助于完善演奏者-乐器交互的原始模型。本项目侧重于音乐交互，但重新利用专业知识的原则在HCI中广泛适用。人类用户的能力不能仅仅通过一般的认知和运动过程来模拟;真实的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那些必须从头开始学习的技术更容易被接受。音乐是一个很好的测试案例，因为乐器培训是广泛和合理的标准化，但研究结果将与其他专家领域。这项奖学金支持PI和两名博士后研究人员的时间。一个博士后将专注于表演者的研究和互动，其他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密切合作将确保其与该社区的相关性。本研究整合硬体设计、数位讯号处理、人机互动、认知科学、音乐学及艺术实践等学科。PI拥有音乐作曲，电子工程和HCI的背景和专业活动，非常适合领导这个多学科项目。

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.