A Transformer-Based Model for Multi-Track Music Generation

Cong Jin, Tao Wang, Shouxun Liu, Yun Tie, Jianguang Li, Xiaobing Li and Simon Lui
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Cong Jin: Communication University of China, China
Tao Wang: Zhengzhou University, China
Shouxun Liu: Communication University of China, China
Yun Tie: Zhengzhou University, China
Jianguang Li: Communication University of China, China
Xiaobing Li: Central Conservatory of Music, China
Simon Lui: Singapore University of Technology and Design, China

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2020, vol. 11, issue 3, 36-54

Abstract: Most of the current works are still limited to dealing with the melody generation containing pitch, rhythm, duration of each note, and pause between notes. This paper proposes a transformer-based model to generate multi-track music including tracks of piano, guitar, and drum, which is abbreviated as MTMG model. The proposed MTMG model is mainly innovated and improved on the basis of transformer. Firstly, the model obtains three target sequences after pairwise learning through learning network. Then, according to these three target sequences, GPT is applied to predict and generate three closely related sequences of instrument tracks. Finally, the three generated instrument tracks are fused to obtain multi-track music pieces containing piano, guitar, and drum. To verify the effectiveness of the proposed model, related experiments are conducted on a pair of comparative subjective and objective evaluation. The encouraging performance of the proposed model over other state-of-the-art models demonstrates its superiority in musical representation.

Date: 2020
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