Decrypt the groove: Audio features of groove and their importance for auditory-motor interactions

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Jan Stupacher
Michael J. Hove
Petr Janata


When we listen to music we often experience a state that can be described as ”˜in the groove’. This state is characterized by the wish or even the urge to move our body to the musical pulse (Janata et al., 2012; Madison, 2006). A previous study showed that high-groove music modulates the excitability of the motor system, whereas no effect of low-groove music was found (Stupacher et al., 2013). But which musical qualities contribute to the feeling of groove? To answer this question, we extracted audio features of 80 song clips with similar instrumentation and correlated them with subjective groove ratings. Song clips and groove ratings of 19 participants were taken from Janata et al. (2012). The following features were extracted with Matlab’s MIR toolbox (Lartillot & Toiviainen, 2007): RMS energy, spectral flux, sub-band flux, pulse clarity (”˜MaxAutocor’ and ”˜Attack’), and event density. Additionally we used the Genesis Loudness toolbox to compute measures of loudness using the loudness model of Glasberg and Moore (2002).

Results showed that groove ratings correlated positively (all ps < .01) with following audio features: RMS energy (r = .37), RMS variability (r = .57), pulse clarity ”˜attack’ (r = .38), spectral flux (r = .34), sub-band flux of band 1 (0-50 Hz, r = .29), and band 2 (50-100 Hz, r = .29). Additionally, groove ratings correlated positively (all ps < .05) with band 3 (100-200 Hz, r = .23), band 5 (400-800 Hz, r = .23), and band 6 (800-1600 Hz, r = .24). The mean loudness of song clips did not affect groove ratings.

Since energy in low frequency bands (Burger et al., 2012; Van Dyck et al., 2013), percussiveness (similar to pulse clarity ”˜attack’), and spectral flux (Burger et al., 2012) have previously been shown to affect motor movements, our results indicate that the experience of groove is a phenomenon predominantly based on auditory-motor interactions (cf. Janata et al., 2012; Stupacher et al., 2013).


Burger, B., Thompson, M. R., Luck, G., Saarikallio, S., & Toiviainen, P. (2012). Music moves us: Beat related musical features influence regularity of music-induced movement. In Proceedings of the 12th International Conference in Music Perception and Cognition and the 8th Triennial Conference of the European Society for the Cognitive Sciences for Music, Thessaloniki, Greece.

Glasberg, B. R., & Moore, B. C. J. (2002). A model of loudness applicable to time-varying sounds. Journal Audio Engineering Society, 50, 331–342.

Janata, P., Tomic, S. T., & Haberman, J. M. (2012). Sensorimotor coupling in music and the psychology of the groove. Journal of Experimental Psychology. General, 141, 54–75.

Lartillot, O., & Toiviainen, P. (2007). A matlab toolbox for musical feature extraction from audio. In Proc. of the 10th Int. Conference on Digital Audio Effects (DAFx-07), Bordeaux, France.

Madison, G. (2006). Experiencing groove induced by music: Consistency and phenomenology. Music Perception, 24, 201–208.
Stupacher, J., Hove, M. J., Novembre, G., Schütz-Bosbach, S., &

Keller, P. E. (2013). Musical groove modulates motor cortex excitability: a TMS investigation. Brain and Cognition, 82, 127–136.

Van Dyck, E., Moelants, D., Demey, M., Deweppe, A., Coussement, P., & Leman, M. (2013). The impact of the bass drum on human dance movement. Music Perception, 30, 349–359.

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