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http://dx.doi.org/10.9717/kmms.2018.21.8.849

Automatic Composition using Time Series Embedding of RNN Auto-Encoder  

Kim, Kyung Hwan (Dept. of Electronics and Information Eng., Hansung University)
Jung, Sung Hoon (Dept. of Electronics and Information Eng., Hansung University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.8, 2018 , pp. 849-857 More about this Journal
Abstract
In this paper, we propose an automatic composition method using time series embedding of RNN Auto-Encoder. RNN Auto-Encoder can learn existing songs and can compose new songs from the trained RNN decoder. If one song is fully trained in the RNN Auto-Encoder, the song is embedded into the vector values of RNN nodes in the Auto-Encoder. If we train a lot of songs and apply a specific vector to the decoder of Auto-Encoder, then we can obtain a new song that combines the features of trained multiple songs according to the given vector. From extensive experiments we could find that our method worked well and generated various songs by selecting of the composition vectors.
Keywords
Automatic Composition; RNN; Auto-Encoder;
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Times Cited By KSCI : 2  (Citation Analysis)
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