Automatic Composition System Based on Transformer-XL

Author:

Li Ze12ORCID,Huang Qing3,Yang Xinhao3ORCID,Chen Qing3,Zhang Li1

Affiliation:

1. School of Computer Science & Technology, Soochow University, Suzhou 215006, China

2. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

3. School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215006, China

Abstract

An automatic composition system that includes music generation and music assessment is designed in this paper. In terms of music generation, we modify the Transformer-XL model for generating music. The Mask mechanism based on Transformer-XL is improved to make the attention of model tend to pay on the bidirectional information, so that the generated popular piano music forms a coherent whole. In terms of music assessment, we combine objective and subjective assessment to judge the generated music in a comprehensive way. Meanwhile, we put forward a new objective assessment method, namely the piano roll classification scoring network. It converts music into pictures and uses classification models in the CV, enabling the network itself to classify and score the generated music. The assessment results from subjective and objective experiments show that by improving the Mask mechanism of Transformer-XL, the model is trained to be better and the generated music could achieve the effect of imitating the real music.

Funder

the Science and Technology Development Plan Project of Suzhou City

Publisher

MDPI AG

Reference47 articles.

1. Hernandez-Olivan, C., and Beltran, J.R. (2021). Music composition with deep learning: A review. arXiv.

2. Briot, J.P., Hadjeres, G., and Pachet, F.D. (2017). Deep learning techniques for music generation—A survey. arXiv.

3. Multimedia intelligence: When multimedia meets artificial intelligence;Zhu;IEEE Trans. Multimed.,2020

4. Eck, D., and Schmidhuber, J. (2002). A First Look at Music Composition Using Lstm Recurrent Neural Networks, Instituto Dalle Molle di Studi Sull’ Intelligenza Artificiale. Technical Rep. No. IDSIA-07-02.

5. Lambert, A., Weyde, T., and Armstrong, N. (August, January 25). Perceiving and predicting expressive rhythm with recurrent neural networks. Proceedings of the 12th International Conference in Sound and Music Computing, Maynooth, Ireland.

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