Bach Style Music Authoring System based on Deep Learning

TL;DR

This paper presents a deep learning-based system that generates Bach-style music using LSTM neural networks, achieving outputs that closely resemble original Bach compositions and are difficult to distinguish from human-created music.

Contribution

It introduces a novel application of LSTM neural networks for Bach-style music generation and demonstrates its effectiveness through comprehensive evaluation.

Findings

Generated music closely mimics Bach's style

AI-composed music is hard to distinguish from original

The system successfully captures Bach's musical features

Abstract

With the continuous improvement in various aspects in the field of artificial intelligence, the momentum of artificial intelligence with deep learning capabilities into the field of music is coming. The research purpose of this paper is to design a Bach style music authoring system based on deep learning. We use a LSTM neural network to train serialized and standardized music feature data. By repeated experiments, we find the optimal LSTM model which can generate imitation of Bach music. Finally the generated music is comprehensively evaluated in the form of online audition and Turing test. The repertoires which the music generation system constructed in this article are very close to the style of Bach's original music, and it is relatively difficult for ordinary people to distinguish the musics Bach authored and AI created.