Modeling the tendency for music to induce movement in humans: First correlations with low-level audio descriptors across music genres.

Abstract

Groove is often described as the experience of music that makes people tap their feet and want to dance. A high degree of consistency in ratings of groove across listeners indicates that physical properties of the sound signal contribute to groove (Madison, 2006). Here, correlations were assessed between listeners' ratings and a number of quantitative descriptors of rhythmic properties for one hundred music examples from five distinct traditional music genres. Groove was related to several different rhythmic properties, some of which were genre-specific and some of which were general across genres. Two descriptors corresponding to the density of events between beats and the salience of the beat, respectively, were strongly correlated with groove across domains. In contrast, systematic deviations from strict positions on the metrical grid, so-called microtiming, did not play any significant role. The results are discussed from a functional perspective of rhythmic music to enable and facilitate entrainment and precise synchronization among individuals.

Audio Features Underlying Perceived Groove and Sensorimotor Synchronization in Music

“…Beat salience (a measure of rhythmic periodicity based on the autocorrelation function of the signal representing the velocities of event onsets) and event density (a measure of the variability in the event onset velocity signal) have been identified as the best predictors of perceived groove across a range of genres (Madison et al, 2011). However, when calculated using the Music Information Retrieval Toolbox (MIR Toolbox) for MATLAB (Lartillot & Toiviainen, 2007), event density failed to predict groove ratings (Stupacher et al, 2013).…”

“…In the first of three studies, we tested the hypothesis that spectral features not only predict movement qualities (as shown by previous studies), but also groove ratings, and we investigated the relationships between different measures of event density (MIR event density & Madison et al, 2011) and rhythmic salience (MIR pulse clarity & Madison et al, 2011). Since musicians use short notes to induce groove , we additionally extracted the attack characteristics of the music stimuli, expecting that fast attack times would be associated with higher groove ratings.…”

“…We selected the following audio features from the MIR Toolbox (Lartillot & Toiviainen, 2007) that were identified as related to music-induced movement or groove in previous studies: Spectral flux, sub-band flux (Alluri & Toiviainen, 2009;Burger et al, 2012;Stupacher et al, 2013), measures of rhythmic clarity (pulse clarity in Burger et al, 2012; beat salience in Madison et al, 2011), event density (Madison et al, 2011), RMS energy (Janata & Tomic, unpublished data), along with a derived measure of the variance in RMS energy. Given the common assumption that RMS is primarily a measure of loudness, we also computed a measure of loudness following the model of Glasberg and Moore (2002).…”

“…The features were selected based on the results of previous studies that examined relations between acoustic descriptors and subjective ratings (Madison et al, 2011;Stupacher et al, 2013), and relations between acoustic descriptors and music-induced movement (Burger et al, 2012; Audio Features of Groove 7…”

Syncopation as Transformation

“…The latter was confirmed by a systematic analysis conducted by Davies, Madison, Silva, & Gouyon (2013) on simple rhythms. Madison et al (2011) claim that tempo alone cannot explain groove. However, Janata, Tomic, & Haberman (2012) conducted an extensive study on sensorimotor coupling, where the computational part of the analysis suggests that music genre and faster tempi have a strong effect on the desire to move along to the music.…”