Abstract
Multitrack music generation technology is becoming more and more mature, but the existing generation technology cannot reach the desired effect in terms of harmony and matching degree, and most of the generated music does not conform to the music theory knowledge. In order to solve these problems, we propose a multitrack music generation network based on transformer to produce music with high musicality under the guidance of music theory rules. This paper uses an improved version of transformer to learn the information inside a single-track sequence and between different tracks. Then, a combination of music theory rules and crossentropy loss is used to guide the training of the generated network, and the well-designed loss objective function is optimized while the discrimination network is trained. Compared with other multitrack music generation models, the validity of our model is proved.
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