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

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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.