An Ergonomic Model of Keyboard Fingering for Melodic Fragments

Abstract

The fingerings used by keyboard players are determined by a range of ergonomic (anatomic/motor), cognitive, and music-interpretive constraints. We have attempted to encapsulate the most important ergonomic constraints in a model. The model, which is presently limited to isolated melodic fragments, begins by generating all possible fingerings, limited only by maximum practical spans between finger pairs. Many of the fingerings generated in this way seldom occur in piano performance. In the next stage of the model, the difficulty of each fingering is estimated according to a system of rules. Each rule represents a specific ergonomic source of difficulty. The model was subjected to a preliminary test by comparing its output with fingerings written by pianists on the scores of a selection of short Czerny studies. Most fingerings recommended by pianists were among those fingerings predicted by the model to be least difficult; but the model also predicted numerous fingerings that were not recommended by pianists. A variety of suggestions for improving the predictive power of the model are explored. A significant upsurge in psychological studies of performance in the past 15 1. Davies, Kenny, and Barbenel (1989) investigated the interface between the trumpet mouthpiece and the mouth.2. We number the fingers according to standard keyboard practice: 1 = thumb, 2 = index finger, ..., and 5 = little finger. Italics distinguish finger numbers from other numbers in the text.

A CSP Approach for Modeling the Hand Gestures of a Virtual Guitarist

“…Several complexity factors were disregarded, which may suggest a guideline for future work, such the fact that chords might also be part of musical patterns together with melodic fragments, and such patterns may be learned, stored and retrieved as a block [6]. Future work will attempt accounting for the fingering of whole blocks of notes, where the case-based reasoning methodology would seem to be profitable: such improvements will lead us to consider real pieces from the classical repertoire, and to explore the automatic expressive performance, where the modeling of instrument/performer interactions plays a central role.…”

A Constraint-based Approach for Annotating Music Scores with Gestural Information

“…Parncutt and colleagues devised a fully functional computational model [25], implemented in a system that annotates fingered positions in melodic fragments for keyboards, while Sayegh [30] proposed an optimization approach based on Viterbi algorithm [38]. Both works share the principle of penalizing difficulties: the fingering process is a search for a combination of positions that pursues an overall effortsaving behavior.…”

“…The cognitive issues involved in keyboard fingering have been raised in the introductive work of Sloboda [31], enhanced with further empirical observations by Clarke and colleagues [32], and modeled with a computational approach by Parncutt, Sloboda, Clarke, Raekallio & Desain [25], lately updated by Jacobs [18].…”

“…Both works share the principle of penalizing difficulties: the fingering process is a search for a combination of positions that pursues an overall effortsaving behavior. The model in [25] addresses the main ergonomic constraints that pianists meet while playing simple melodies, underpinned by several treatises and composers instructions describing the instrumental practice as it evolved through centuries. Fingering is a two-step, "generate and test," process: enumeration of all possible fingerings and weighting of fingerings according to the amount of difficulty.…”

Characterizing Idiomatic Organization in Music: A Theory and Case Study of Musical Affordances

“…On the violin and guitar, for example, a choice of strings may be available. On the piano, the pianist may choose to finger a passage in one of several ways: depending on the choice of fingering, a piano passage may be rendered either easier or more difficult (Parncutt, et al, 1997;Sloboda, et al, 1998). As the number of performance alternatives increase, the task of modeling a performance becomes more complex since the model must emulate sophisticated performance decisions made by the musician (Radicioni, 2005).…”