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 Artificial Intelligence field which is still being developed. One of its subfields 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 differences 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
