Sampled drums can be used as an affordable way of creating
human-like drum tracks, or perhaps more interestingly,
can be used as a mean of experimentation with rhythm
and groove. Similarly, AI-based drum generation tools can
focus on creating human-like drum patterns, or alternatively,
focus on providing producers/musicians with means
of experimentation with rhythm. In this work, we aimed
to explore the latter approach. To this end, we present a
suite of Transformer-based models aimed at ...
Sampled drums can be used as an affordable way of creating
human-like drum tracks, or perhaps more interestingly,
can be used as a mean of experimentation with rhythm
and groove. Similarly, AI-based drum generation tools can
focus on creating human-like drum patterns, or alternatively,
focus on providing producers/musicians with means
of experimentation with rhythm. In this work, we aimed
to explore the latter approach. To this end, we present a
suite of Transformer-based models aimed at completing audio
drum loops with stylistically consistent symbolic drum
events. Our proposed models rely on a reduced spectral
representation of the drum loop, striking a balance between
a raw audio recording and an exact symbolic transcription.
Using a number of objective evaluations, we explore the validity
of our approach and identify several challenges that
need to be further studied in future iterations of this work.
Lastly, we provide a real-time VST plugin that allows musicians/
producers to utilize the models in real-time production
settings.
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