With years of experience and practice, musicians gain implicit knowledge about where to speed up or slow down in music, when to lengthen or shorten notes, and when to place accents for special effect. As listeners, we are accustomed to these micro-scale variations in music, and are not necessarily aware of the extent to which performers vary their timing and tempo for expressive effect. Although the exact characteristics of a performance depend on a musician's individual interpretation of the music, there are certain regularities or rules that are commonly observed across different performances within a particular genre. The rise of digital tools in music analysis has allowed a (semi-)automatic investigation of such performance characteristics, and consequently has deepened our understanding of performance processes.
Our presentation overviews an interactive music performance analysis demonstration that allows participants to explore performance expression in two domains: jazzy swing and romantic rubato. The goal is for participants to increase their sensitivity to what expressive music performance entails by interactively engaging with different performances of the same music. In the first demonstration, participants can listen to, and subsequently tap along with, a jazzy drum pattern played with swing by different performers at a range of tempi. The resulting time-sequence of taps is captured and compared with note-onset information derived from the recordings of expert performers. Feedback to the participant is derived from a histogram distribution of inter-onset-intervals, in particular considering the ratio between the median duration of the two shorter notes in the swing pattern. In the second demonstration, participants listen to and tap along with different performances of an excerpt of a piano prelude by Chopin. As before, the resulting tap-sequence is compared with note onset information from expert performances. Feedback to the participant is now based on the tempo trajectory across the music, in particular looking at the degree of quickening or slackening, and the position of duration peaks and troughs.
Trialled with prospective music students at University Open Day sessions, this work additionally has a wider relevance to the development of digital tools for automated feedback on timing processes in music (and possibly beyond).