An Attempt to Generate Mozart's Piano Compositions Based on an LSTM Model

LSTM, proposed by German computer scientists Sepp Hochreiter and Jürgen Schmidhuber in 1997 3, has shown outstanding performance in various tasks, especially in tasks that require capturing long-term dependencies. These tasks include language modeling and machine translation in the field of natural language processing, as well as stock price prediction. This project aims to explore the potential of LSTM models in the field of music generation by analyzing Mozart's classical piano works using music21 and creating a training dataset. The project uses LSTM to learn this dataset, adjusting the forget gate, input gate, and different training iteration times to generate diversified outputs. Finally, the generated music data was manually selected and labeled.