Real-Time Drum Accompaniment Using Transformer Architecture
Haki B, Nieto M, Pelinski T, Jordà S
Proceedings of the 3rd Conference on AI Music Creativity (AIMC 2022)
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.
Haki B, Nieto M, Pelinski T, Jordà S. Real-Time Drum Accompaniment Using Transformer Architecture. In: AA. VV.. Proceedings of the 3rd Conference on AI Music Creativity (AIMC 2022). 1 ed. AIMC; 2022. p. 1-10.