Emotional Piano Melodies Generation Using Long Short-Term Memory

Abstract

One of the tremendous topics in the music industry is an automatic music composition. In this study, we aim to build an architecture that shows how LSTM models compose music using the four emotional piano datasets. The architecture consists of four steps: data collection, data preprocessing, training the models with one and two hundred epochs, and evaluation by loss analysis. From the result of this work, the model trained for 200 epochs give the lowest loss error rate for the composing of emotional piano music. Finally, we generate four emotional melodies based on the result.