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.
@inproceedings{Haki2023Completing,author={Haki, Behzad and Pelinski, Teresa and Nieto, Marina and Jorda, Sergi},booktitle={Proceedings of the International Conference on New Interfaces for Musical Expression (NIME) 2023},year={2023},month=apr,publisher={NIME},title={{Completing Audio Drum Loops with Symbolic Drum Suggestions}},}
