作者
Dimos Makris, Maximos Kaliakatsos-Papakostas, Ioannis Karydis, Katia Lida Kermanidis
发表日期
2019/6/1
期刊
Neural Computing and Applications
卷号
31
期号
6
页码范围
1793-1804
出版商
Springer London
简介
Machine learning has shown a successful component of methods for automatic music composition. Considering music as a sequence of events with multiple complex dependencies on various levels of a composition, the long short-term memory-based (LSTM) architectures have been proven to be very efficient in learning and reproducing musical styles. The “rampant force” of these architectures, however, makes them hardly useful for tasks that incorporate human input or generally constraints. Such an example is the generation of drums’ rhythms under a given metric structure (potentially combining different time signatures), with a given instrumentation (e.g. bass and guitar notes). This paper presents a solution that harnesses the LSTM sequence learner with a feed-forward (FF) part which is called the “Conditional Layer”. The LSTM and the FF layers influence (are merged into) a single layer making the …
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