Real-time drum accompaniment using transformer architecture
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- dc.contributor.author Haki, Behzad
- dc.contributor.author Nieto, Marina
- dc.contributor.author Pelinski, Teresa
- dc.contributor.author Jordà Puig, Sergi
- dc.date.accessioned 2023-11-15T07:17:37Z
- dc.date.available 2023-11-15T07:17:37Z
- dc.date.issued 2022
- dc.description Comunicació presentada a AIMC 2022, celebrada del 13 al 15 de setembre de 2022 en línia.
- dc.description.abstract This paper presents a real-time drum generation system capable of accompanying a human instrumentalist. The drum generation model is a transformer encoder trained to predict a short drum pattern given a reduced rhythmic representation. We demonstrate that with certain design considerations, the short drum pattern generator can be used as a real-time accompaniment in musical sessions lasting much longer than the duration of the training samples. A discussion on the potentials, limitations and possible future continuations of this work is provided.
- dc.format.mimetype application/pdf
- dc.identifier.citation Haki B, Nieto M, Pelinski T, Jordà S. Real-time drum accompaniment using transformer architecture. In: Proceedings of the 3rd Conference on AI Music Creativity (AIMC 2022); 2022 13-15 Sep; online. [s.l.]: AI Music Creativity; 2022. 10 p. DOI: 10.5281/zenodo.7088343
- dc.identifier.doi http://dx.doi.org/10.5281/zenodo.7088343
- dc.identifier.uri http://hdl.handle.net/10230/58270
- dc.language.iso eng
- dc.publisher AI Music Creativity
- dc.rights Creative Commons Attribution 4.0 International
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0
- dc.title Real-time drum accompaniment using transformer architecture
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/publishedVersion