DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
This addresses the problem of creative music generation for musicians and producers, offering an incremental improvement by integrating human feedback into a deep learning system.
The paper tackles the problem of generating drum loops by developing DeepDrummer, a tool that uses deep learning and active learning to learn user preferences from few interactions, enabling efficient exploration of musical ideas. In a study with 25 participants, it demonstrated convergence to user preferences after a small number of interactions.
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.