Speaker
Description
Groove, defined as the pleasurable urge to move in response to music, is a central dimension of rhythmic perception. While previous research has examined the contributions of syncopation, microtiming, and tempo to groove, the role of sound dynamics remains poorly understood. This study investigates how variations in sound level across drum patterns influence perceived groove, and whether dynamic range compression can be used to enhance it. Twenty participants rated the groove, pleasure, and complexity of 159 short snare-drum sequences designed to systematically vary dynamic structure. A linear mixed-effects model revealed significant effects of dynamic pattern on groove ratings, with rankings that remained stable across different levels of gain variability. To predict groove from sound dynamics, we introduce a fuzzy representation of gain levels combined with a ridge regression model. A transition-based feature representation, encoding sequential dependencies between successive gain events, outperformed a distribution-based approach, suggesting that the temporal ordering of dynamic levels is a key perceptual cue. Using the trained model in a generative framework, we further show that modifying sound dynamics with dynamic compression can significantly increase predicted groove across a wide range of dynamic configurations. These findings suggest that compression could be used not only for loudness control, but also as a perceptually motivated tool for shaping groove in live music contexts.