Composing Multi-Instrumental Music with Recurrent Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10408039" target="_blank" >RIV/00216208:11320/19:10408039 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IJCNN.2019.8852430" target="_blank" >https://doi.org/10.1109/IJCNN.2019.8852430</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2019.8852430" target="_blank" >10.1109/IJCNN.2019.8852430</a>
Alternative languages
Result language
angličtina
Original language name
Composing Multi-Instrumental Music with Recurrent Neural Networks
Original language description
We propose a generative model for artificial composition of both classical and popular music with the goal of producing music as well as humans do. The problem is that music is based on a highly sophisticated hierarchical structure and it is hard to measure its quality automatically. Contrary to other's work, we try to generate a symbolic representation of music with multiple different instruments playing simultaneously to cover a broader musical space. We train three modules based on LSTM networks to generate the music; a lot of effort is put into reducing the high complexity of multi-instrumental music representation by a thorough musical analysis. We believe that the proposed preprocessing techniques and symbolic representation constitute a useful resource for future research in this field
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GJ17-10090Y" target="_blank" >GJ17-10090Y: Network Optimization</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 International Joint Conference on Neural Networks (IJCNN)
ISBN
978-1-72811-985-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Neuveden
Event location
Budapešť, Maďarsko
Event date
Jul 14, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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