Learn to Play: Computational Assessment of Musical Playability for Users' Practice

这项针对Impact and Engagement的后续资金提案是基于AHRC的数字转型项目“改造音乐学”(AH/L006820/1)和琵琶音乐电子语料库项目(最近的项目是“露天鲁特音乐(ECOLM III)”AH/H037829/1)的研究。它探索了音乐的“可播放性”的概念。通过开发一个系统来评估展示作品的难度，然后使用该系统根据系统判断为棘手的音乐中的段落按需创建一套练习练习，它将帮助学生学习演奏乐器(长笛、吉他或文艺复兴时期的琵琶)。吉他是当今世界上使用最广泛的乐器，而互联网上提供了数量令人眼花缭乱的“标签”(乐谱以表格的形式表示)，不需要任何正式的乐谱知识。Tablature提供了关于手指放置以形成和弦或旋律以及它们应该被演奏的顺序的说明。这是一个经得起时间考验的系统，至少从15世纪开始就已经使用了数百年，对器乐教学特别有用，特别是在早期阶段。网上有大量的音乐，我们创建的系统将帮助音乐家找到适合他们技能水平的音乐。该系统将分析吉他(古典和其他风格)和文艺复兴时期鲁特琴(ECOLM中已经有c10,000首曲子的语料库)音乐片段的表格化版本的可演奏性能。使用表格中显示的手部伸展和位置移动的测量方法，我们将计算单个和弦和弦之间过渡的可演奏指数。长笛是另一种在自学者和年轻人中非常受欢迎的乐器，特别是在学校；根据哈克尼音乐服务的数据，我们估计仅在伦敦的学校就有超过3000名非初学长笛的学生在上课。我们将在Fiebrink合作的早期工作的基础上对笛子音乐中的难度进行建模，这是一个非常有用的起点，因为音乐的更简单的质地使我们能够专注于它的旋律方面，而不是和弦(如吉他或琵琶)。然后，我们将使用标准的机器学习技术来构建可播放性模型，以识别未知长笛、吉他和琵琶作品中的困难段落。它们还将被用来对作品进行评级(基于最具技术挑战性的段落的难度)，并将结果与音乐出版商在其目录中列出的评级进行比较。该项目的主要成果是概念验证演示器，然后将使用简单的算法根据这些段落生成全新的练习，供学生练习。所有上述内容将由我们的用户社区-各级演奏者以及长笛、吉他和琵琶教师-进行评估。音乐将在我们的音乐行业合作伙伴Tido Music提供的高质量图形用户界面中呈现。目前用于一些教育包，主要针对业余钢琴家，它将被改造为与由Goldsmits开发和维护的可玩性评估和练习生成后端进行远程通信。通过这种方式，我们的模型可以通过专业的用户界面从一开始就进行测试，并使用来自Tido音乐库的音乐配乐(访问限制完全由Tido控制)，或者来自其他地方的音乐配乐，而不会损害版权所有权。我们将为专业和业余音乐人举办一个研讨会，包括那些参与Beta测试者的人，与它的设计者和开发者讨论他们对该系统的评估。然后，这些反馈将被用作一份完整的提案的材料，该提案旨在为创新英国提供资金，以进行进一步的研究和开发，将这项工作超越概念验证，转化为商业上可行的产品。

This Follow-On Funding for Impact and Engagement proposal is based on research from the AHRC Digital Transformations project, 'Transforming Musicology' (AH/L006820/1), and the Electronic Corpus of Lute Music project, most recently as 'Lute Music in the Open (ECOLM III)', AH/H037829/1. It explores the concept of 'playability' of music. By developing a system to assess the difficulty of a displayed piece, and then using this system to create on demand a set of practice exercises based on passages within the music judged to be tricky by the system, it will help students learning to play an instrument (flute, guitar, or renaissance lute). The guitar is the most widespread instrument in the world today, and the internet provides a literally bewildering number of 'tabs' (scores notated in the format known as tablature) requiring no formal knowledge of music notation. Tablature provides instructions about the placement of fingers to form chords or melodies and the sequence in which they should be played. It is a system that has stood the test of time, and has been used for hundreds of years, at least since the 15th century, and is particularly useful for instrumental teaching, especially in the early stages.There is a vast amount of music available online and the system we create will help musicians find music to suit their skill level. The system will analyse the playability of tablature versions of pieces of music for guitar (classical and other styles) and for renaissance lute (we already have a corpus of c10,000 pieces in ECOLM). Using measures based on hand-stretches and position-shifts indicated in the tablature we'll compute indexes of playability of individual chords and transitions between them.The flute is another very popular instrument among self-learners and young people, especially in schools; based on figures from the Hackney Music Service, we estimate that over 3,000 non-beginner flute students take lessons in London schools alone. We'll build on earlier work carried out by co-I Fiebrink on the modelling of difficulty in flute music, a very useful starting point, since the simpler texture of the music allows us to focus on its melodic aspects rather than on chords (as on guitar or lute). We'll then use standard machine-learning techniques to build models of playability to identify difficult passages in unknown flute, guitar and lute pieces. They will also be used to grade pieces (based on the difficulty of the most technically-challenging passages) and the results compared with the grades listed by music publishers in their catalogues. The proof-of-concept demonstrator forming the main output of the project will then use simple algorithms to generate entirely new exercises derived from these passages for practising by a student.All the above will be evaluated by our user community - players at various levels and flute, guitar and lute teachers.The music will be presented within a high-quality graphical user interface provided by our music-industry partner, Tido Music. Currently used for a number of educational packages, mostly aimed at amateur pianists, it will be adapted to communicate remotely with the playability estimation and exercise generation back-end developed and maintained by Goldsmiths. This way our models can be tested from the outset with a professional user-interface, and use musical scores from the Tido music library (access restrictions entirely under Tido's control), or from elsewhere, without compromising rights ownership.The lessons learned will be applied directly in two ways. We shall hold a workshop for professional and amateur musicians, including those involved as beta-testers, to discuss their assessment of the system with its designers and developers. This feedback will then be used as material for a full proposal to Innovate UK for funds to carry out further research and development to take this work beyond proof of concept to a commercially viable product.