EME33: A Dataset of Classical Piano Performances Guided by Expressive Markings with Application in Music Rendering

Expressive performance in classical music plays a crucial role in shaping interpretations of musical pieces. However, existing datasets often provide limited attention to expressive markings in piano performances. This research addresses this gap by developing the Expressive Markings and Emotions 33 (EME33) dataset, which captures expressive piano performances in MIDI format, annotated with dynamic markings, expression markings, and additional emotional expressions. To validate the dataset’s applicability, we employ a Long Short-Term Memory (LSTM) model, which is suited to the size of our dataset, to render expressiveness in music. The results demonstrate the model’s effectiveness in capturing and reproducing expressive performance, highlighting the potential of the EME33 dataset for future research in music information retrieval.