  We introduce DrummerNet, a drum transcription system that is trained in an
unsupervised manner. DrummerNet does not require any ground-truth transcription
and, with the data-scalability of deep neural networks, learns from a large
unlabeled dataset. In DrummerNet, the target drum signal is first passed to a
(trainable) transcriber, then reconstructed in a (fixed) synthesizer according
to the transcription estimate. By training the system to minimize the distance
between the input and the output audio signals, the transcriber learns to
transcribe without ground truth transcription. Our experiment shows that
DrummerNet performs favorably compared to many other recent drum transcription
systems, both supervised and unsupervised.
