A Deep Learning Approach to generate Beethoven's 10th Symphony

Luidwig van Beethoven composed his symphonies between 1799 and 1825,
when he was writing his Tenth symphony. As we dispose of a great amount
of data belonging to his work, the purpose of this project is to work on the
possibility of extracting patterns on his compositional model and generate
what would have been his last symphony, the Tenth.
Computational creativity is an Articial Intelligence eld which is still
being developed. One of its subelds is music generation, to which this
project belongs. Also, there is an open discussion about the belonging of the
creativity, to the machine or the programmer.
Firstly we have extracted all the symphonies' scores information, structuring
them by instrument. Then we have used Deep Learning techniques to
extract knowledge from the data and later generate new music. The neural
network model is built based on the Long Short-Therm Memory (LSTM)
neural networks, which are distinguished from others since these ones contain
a memory module. After training the model and predict new scores, the
generated music has been analyzed by comparing the input data with the
results, and establishing dierences between the generated outputs based on
the training data used to obtain them. The result's structure depends on the
symphonies used for training, so obtained music presents Beethoven's style
characteristics.