  The state-of-the-art methods for drum transcription in the presence of
melodic instruments (DTM) are machine learning models trained in a supervised
manner, which means that they rely on labeled datasets. The problem is that the
available public datasets are limited either in size or in realism, and are
thus suboptimal for training purposes. Indeed, the best results are currently
obtained via a rather convoluted multi-step training process that involves both
real and synthetic datasets. To address this issue, starting from the
observation that the communities of rhythm games players provide a large amount
of annotated data, we curated a new dataset of crowdsourced drum
transcriptions. This dataset contains real-world music, is manually annotated,
and is about two orders of magnitude larger than any other non-synthetic
dataset, making it a prime candidate for training purposes. However, due to
crowdsourcing, the initial annotations contain mistakes. We discuss how the
quality of the dataset can be improved by automatically correcting different
types of mistakes. When used to train a popular DTM model, the dataset yields a
performance that matches that of the state-of-the-art for DTM, thus
demonstrating the quality of the annotations.
