In expressive piano performance, the performer emphasises a melody by increasing its intensity and by anticipating it by some tens of milliseconds (melody lead). In this contribution, we continue previous research on the influence of asynchrony and intensity variation on the perceived salience of a particular tone or voice with three experiments. In Experiment I, threetone piano chords are presented with each of the three tones simultaneously manipulated in timing and intensity by up to 55 ms and +30/--22 MIDI velocity units. Loudness ratings depended mainly on relative intensity and relatively little on timing (e.g., anticipated tones were sometimes rated louder than delayed ones). The lower voice was generally rated louder than the middle voice. In Experiment II, a sequence of chords produced similar results; streaming enhanced the effect of asynchrony only marginally. In Experiment III, a short musical excerpt by Chopin was presented. Again, intensity was the dominating cue. In contrast to previous findings, a melody that was both delayed and louder in intensity was rated significantly louder than a melody that was simultaneous and louder.

The paper reports on first steps towards automated computational analysis of a unique and unprecedented corpus of symbolic performance data. In particular, we focus on between-hand asynchronies – an expressive device that plays an important role particularly in Romantic music, but has not been analyzed quantitatively in any substantial way. The historic data were derived from performances by the renowned pianist Nikita Magaloff, who played the complete work of Chopin live on stage, on a computer-controlled grand piano. The mere size of this corpus (over 320,000 performed notes or almost 10 hours of continuous performance) challenges existing analysis approaches. The computational steps include score extraction, score-performance matching, definition and measurement of the analyzed features, and a computational visualization tool. We then present preliminary data to demonstrate the potential of our approach for future computational modeling and its application in computational musicology. 1