DeepDrum: An Adaptive Conditional Neural Network

Autor:	Makris, Dimos, Kaliakatsos-Papakostas, Maximos, Kermanidis, Katia Lida
Rok vydání:	2018
Předmět:	Computer Science - Sound Computer Science - Information Retrieval Electrical Engineering and Systems Science - Audio and Speech Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Considering music as a sequence of events with multiple complex dependencies, the Long Short-Term Memory (LSTM) architecture has proven very efficient in learning and reproducing musical styles. However, the generation of rhythms requires additional information regarding musical structure and accompanying instruments. In this paper we present DeepDrum, an adaptive Neural Network capable of generating drum rhythms under constraints imposed by Feed-Forward (Conditional) Layers which contain musical parameters along with given instrumentation information (e.g. bass and guitar notes). Results on generated drum sequences are presented indicating that DeepDrum is effective in producing rhythms that resemble the learned style, while at the same time conforming to given constraints that were unknown during the training process. Comment: 2018 Joint Workshop on Machine Learning for Music. The Federated Artificial Intelligence Meeting (FAIM), a joint workshop program of ICML, IJCAI/ECAI, and AAMAS
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.06127 Zobrazit plný text záznamu View this record from Arxiv