Sony CSL Paris develops technology for AI-assisted music production. The goal is not to replace musicians, but to provide them with better tools to be more efficient in realizing their creative ideas. DrumNet is based on an artificial neural network which learns rhythmic relationships between different instruments and encodes these relationships in a 16-dimensional style space. A similar example is the Logic X Drummer, allowing the user to specify the playing style by navigating a two-dimensional space. The difference of DrumNet to the Logic X Drummer, however, is that it dynamically adapts to the existing music. In its current form, DrumNet can either autonomously generate kick drum tracks (following the statistics of the training data), be controlled by manually navigating the style space, or be used to extract a style from an existing piece.

As opposed to many other generative music technologies, we aim to directly use existing audio tracks as input to which we generate the kick drum track as audio output. Using audio input directly makes DrumNet more useful for music production than models based on MIDI input. We show the generality of the model, by providing many examples of full songs with different generated kick drum tracks on this website. For a proof-of-concept, we trained the model only on kick drum rhythms, but we are currently extending the model to generate a whole drum set.

Check out the sound examples.

Authors: Stefan Lattner, Maarten Grachten

Related Publications: High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction