Southall, Carl, Wu, Chih-Wei, Alexander Lerch, Hockman, Jason A. MDB Drums --- An Annotated Subset of MedleyDB for Automatic Drum Transcription. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Late Breaking Demo (Extended Abstract). International Society for Music Information Retrieval (ISMIR), Suzhou, 2017.
In this paper we present MDB Drums, a new dataset for automatic drum transcription (ADT) tasks. This dataset is built on top of the MusicDelta subset of the MedleyDB dataset, taking advantage of real-world recordings in multi-track format. The dataset is comprised of a variety of genres, providing a balanced pool for developing and evaluating ADT models with respect to various musical styles. To reduce the cost of the labor-intensive process of manual annotation, a semi-automatic process was utilised in both the annotation and quality control processes. The pre sented dataset consists of 23 tracks with a total of 7994 onsets. These onsets are divided into 6 classes based on drum instruments or 21 subclasses based on playing techniques. Every track consists of a drum-only track as well as multiple accompanied tracks, enabling audio files containing different combinations of instruments to be used in the ADT evaluation process.