  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.
