Piano Music Generation with Deep Learning Transformer Models

Abstract

William Wordsworth famously wrote in "The Solitary Reaper": "The music in my heart I bore, long after it was heard no more". The emotional impact of music, particularly piano music, has transcended cultural barriers and touched people from all walks of life for centuries. In recent years, the composition of music with the assistance of artificial intelligence has become a notable area of interest that has garnered significant attention. Among multiple deep learning models proposed, the Transformer has been a prominent approach for generating longer piano performances. This thesis delves into the capabilities of symbolic piano music generation with two noteworthy Transformer models, Music Transformer and Perceiver-AR. We explore training these models in six different datasets of various sizes and musical genres, generate large-scale number of outputs for each trained model and evaluate them objectively and subjectively. We investigate the impact of training datasets, model types and trained models on the music generation process. We also examine the correlations of objective and subjective evaluation metrics and propose a set of primary subjective quality indicators for music generated by artificial intelligence models. Finally, we suggest possible improvements and areas for future research.