Correlated microtiming deviations in jazz and rock music

Musical rhythms performed by humans typically show temporal fluctuations. While they have been characterized in simple rhythmic tasks, it is an open question what is the nature of temporal fluctuations, when several musicians perform music jointly in all its natural complexity. To study such fluctuations in over 100 original jazz and rock/pop recordings played with and without metronome we developed a semi-automated workflow allowing the extraction of cymbal beat onsets with millisecond precision. Analyzing the inter-beat interval (IBI) time series revealed evidence for two long-range correlated processes characterized by power laws in the IBI power spectral densities. One process dominates on short timescales ($t < 8$ beats) and reflects microtiming variability in the generation of single beats. The other dominates on longer timescales and reflects slow tempo variations. Whereas the latter did not show differences between musical genres (jazz vs. rock/pop), the process on short timescales showed higher variability for jazz recordings, indicating that jazz makes stronger use of microtiming fluctuations within a measure than rock/pop. Our results elucidate principles of rhythmic performance and can inspire algorithms for artificial music generation. By studying microtiming fluctuations in original music recordings, we bridge the gap between minimalistic tapping paradigms and expressive rhythmic performances

Scheme of the power-spectral density (PSD) of inter-beat interval (IBI) time series.

<p>A. The combination of a long-range correlated clock and motor process (dashed lines) superpose to a V-shaped PSD of the IBIs (solid line). The characteristic frequency (or time scale, see upper axis) at the minimum determines the turnover between the clock-dominated and motor-dominated regime. B. Same as panel A, but assuming that both, the clock and motor process are uncorrelated. C. Sketch of genre-induced differences in the PSD.</p

Scaling exponents (<i>β</i>) obtained from analyzing the PSDs of IBI time series.

<p>A. Neither the clock nor the motor process is random but clearly long-range persistent, i.e. <i>β</i>_<i>C</i> > 0, −<i>β</i>_<i>M</i> < 2. *** denotes <i>p</i> ≪ 10⁻³ (significance obtained by bootstrapping). B. Genre-dependence of the scaling exponents. The motor process showed significantly stronger long-range persistence in rock/pop (R) than in jazz (J). The box plot depicts the median in red, boxes at the first and third quartile, whiskers at 1.5 ⋅ IQR (interquartile range), and circles represent outliers. ** denotes <i>p</i> = 0.001, and n.s. denotes <i>not significant</i>.</p

Fit parameters of the unpaced musical recordings by genre (jazz: red, rock/pop: blue).

<p>Both, the music performances and our studio recordings showed consistent tendencies (median (SD) given as black lines (colored ranges) for the individual parameters). Most prominently, the motor persistence <i>β</i>_<i>M</i> is genre-dependent.</p

Workflow for the estimation of the beat-onset time series from one channel of a music recording.

<p>Workflow for the estimation of the beat-onset time series from one channel of a music recording.</p