Drum Fills Detection and Generation

Drum fills are essential in the drummer’s playing. They regularly restore energy and announce the transition to a new part of the song. This aspect of the drums has not been explored much in the field of MIR because of the lack of datasets with drum fills labels. In this paper, we propose two methods to detect drum fills along a song, to obtain drum fills context information. The first method is a logistic regression which uses velocity-related handcrafted data and features from the latent space of a variational autoencoder. We give an analysis of the classifier performance regarding each features group. The second method, rule-based, considers a bar as a fill when a sufficient difference of notes is detected with respect to the adjacent bars. We use these two methods to extract regular pattern/ drum fill couples in a big dataset and examine the extraction result with plots and statistical test. In a second part, we propose a RNN model for generating drum fills, conditioned by the previous bar. Then, we propose objective metrics to evaluate the quality of our generated drum fills, and the results of a user study we conducted. Please go to https://frederictamagnan.github.io/drumfills/ for details and audio examples.