An automatic music generation method based on RSCLN_Transformer network

With the development of artificial intelligence and deep learning, a large number of music generation methods have been proposed. Recently, Transformer has been widely used in music generation. However, the structural complexity of music puts forward higher requirements for music generation. In this paper, we propose a new automatic music generation network which consists of a Recursive Skip Connection with Layer Normalization (RSCLN) model, a Transformer-XL model and a multi-head attention mechanism. Our method not only alleviates the gradient vanishing problem in the model training, but also increases the ability of the model to capture the correlation of music information before and after, so as to generate music works closer to the original music style. Effectiveness of the RSCLN_Transformer-XL music automatic generation method is verified through music similarity evaluation experiments using music structure similarity and listening test. The experimental results show that the RSCLN_Transformer-XL music automatic generation model can generate better music than the Transformer-XL model.