Conclution
We have seen the trends of machine learning, now is the turn of deep
learning. Deep learning is used in a variety of field, and we can see
every sign of it in our daily life. NPL, Computer Vision are not all, we
have learned about deep learning in the audio domain which it can be
used to compose music. Although, it is kind of like an entertainment.
What we should see is the potential of it, if it was the start of the
revolution of a new form of art–generate by machine. Our review is
based on Sageev Oore, using MIDI file dataset and events that similar to
MIDI events as the representation of music. The model they proposed is
aiming to not only generate music but also music with expressive timing
and dynamics. And the network structure is the LSTM network which is
good with dealing sequences data. During our research, we have trained
our own dataset also use piano performance. For the feature work, we
consider using different types of music as the training set, see what
kind of music would generate.
References
- Oore, S., Simon, I., Dieleman, S. et al. This time with feeling:
learning expressive musical performance. Neural Comput & Applic 32,
955–967 (2020). https://doi.org/10.1007/s00521-018-3758-9
- File format description–(.mid) Standard MIDI File Format.
http://faydoc.tripod.com/formats/mid.htm
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- Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi
Anna Huang,Sander Dieleman, Erich Elsen, Jesse Engel, and Douglas Eck.
”Enabling Factorized Piano Music Modeling and Generation with the
MAESTRO Dataset.” In International Conference on Learning
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Proceedings. Vol. 80. 2017.
- Walder, Christian, and Dongwoo Kim. ”Computer assisted composition
with recurrent neural networks.” arXiv preprint arXiv:1612.00092
(2016).