What musicians do to induce the sensation of groove in simple and complex melodies, and how listeners perceive it

Abstract

Groove is the experience of wanting to move when hearing music, such as snapping fingers or tapping feet. This is a central aspect of much music, in particular of music intended for dancing. While previous research has found considerable consistency in ratings of groove across individuals, it remains unclear how groove is induced, that is, what are the physical properties of the acoustic signal that differ between more and less groove-inducing versions. Here, we examined this issue with a performance experiment, in which four musicians performed six simple and six complex melodies in two conditions with the intention of minimizing and maximizing groove. Analyses of rhythmical and temporal properties from the performances demonstrated some general effects. For example, more groove was associated with more notes on faster metrical levels and syncopation, and less groove was associated with deadpan timing and destruction of the regular pulse. We did not observe that deviations from the metrical grid [i.e., micro-timing (MT)] were a predictor of groove. A listener experiment confirmed that the musicians' manipulations had the intended effects on the experience of groove. A Brunswikian lens model was applied, which estimates the performer-perceiver communication across the two experiments. It showed that the communication achievement for simple melodies was 0.62, and that the matching of performers' and listeners' use of nine rhythmical cues was 0.83. For complex melodies with an already high level of groove, the corresponding values were 0.39 and 0.34, showing that it was much more difficult to "take out" groove from musical structures designed to induce groove.

Keywords: groove; micro-timing; movement; music; musicians; rhythm; syncopation.

Figure 1

Example of a 4/4 metrical template used in the SG calculation of the amount of syncopation. At the bottom, the syncopation weights for each metrical position are shown as black bars. The dashed arrows depict the amplitude differences for the 8th note of the second beat in the bar, as an example of the amplitude differences taken for each metrcial position during the calculation of the algorithm.

Figure 2

Performance parameters as a function of groove condition and melody type, across performers. Panel (A) shows the number of notes and the SG syncopation estimate, Panels (B,C) show the mean proportion of events that started and ended on 8th and 16th note positions, respectively, and Panel (D) shows the mean proportion of 8th and 16th note event durations. Error bars depict 0.95% confidence intervals.

Figure 3

Perceived groove as a function of groove condition and melody type, across listeners. Error bars depict 0.95% confidence intervals.