Title: Generování žánrově specifické hudební transkripce Antonína Dvořáka prostřednictvím variačního autoenkodéru
Variant title:
- Generating genre-specific musical transcriptions of Antonín Dvořák through a variational autoencoder
Source document: Musicologica Brunensia. 2021, vol. 56, iss. 2, pp. 49-61
Extent
49-61
-
ISSN1212-0391 (print)2336-436X (online)
Persistent identifier (DOI): https://doi.org/10.5817/MB2021-2-5
Stable URL (handle): https://hdl.handle.net/11222.digilib/144818
Type: Article
Language
License: CC BY-SA 4.0 International
Notice: These citations are automatically created and might not follow citation rules properly.
Abstract(s)
Apart from traditional deep learning tasks such as pattern recognition, stock price prediction, and machine translation, this method also finds practical application within algorithmic composition. This paper explores the use of a generative model based on unsupervised learning of a musical style from a pre-selected corpus and the subsequent prediction of samples from the estimated distribution. The model uses a Long Short-Term Memory neural network whose training data contains genre-specific melodies in symbolic representation.
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