17th International Society for Music Information Retrieval Conference (ISMIR)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Music transcription is a core task in the field of music
information retrieval. Transcribing the drum tracks of mu-
sic pieces is a well-defined sub-task. The symbolic repre-
sentation of a drum track contains much useful information
about the piece, like meter, tempo, as well as various style
and genre cues. This work introduces a novel approach for
drum transcription using recurrent neural networks. We
claim that recurrent neural networks can be trained to iden-
tify the onsets of percussive instruments based on general
properties of their sound. Different architectures of recur-
rent neural networks are compared and evaluated using a
well-known dataset. The outcomes are compared to results
of a state-of-the-art approach on the same dataset. Further-
more, the ability of the networks to generalize is demon-
strated using a second, independent dataset. The exper-
iments yield promising results: while F-measures higher
than state-of-the-art results are achieved, the networks are
capable of generalizing reasonably well.
Forschungs-