Automated Music Generation Using Recurrent Neural Networks

被引:0
作者
Czyz, Mateusz [1 ]
Kedziora, Michal [1 ]
机构
[1] Wroclaw Univ Sci & Technol, Fac Comp Sci & Management, Wroclaw, Poland
来源
THEORY AND ENGINEERING OF DEPENDABLE COMPUTER SYSTEMS AND NETWORKS, DEPCOS-RELCOMEX 2021 | 2021年 / 1389卷
关键词
Neural networks; LSTM; Recurrent neural networks; Music; Machine learning;
D O I
10.1007/978-3-030-76773-0_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The paper aims to devise a set of machine learning models based on recurrent neural networks with emphasis on utilizing LSTM layers. These models are meant to be able to generate musical features such as melody notes or chords in sequence, or in other words generate music. Authors has decided to implement methods for music notation generation. Moreover, the paper contains a thorough description of the preprocessing of the obtained dataset along with the used ML technology and the latest research in related fields. In the paper, the authors elaborate on the process of training the devised models and example results of prediction done by the neural networks.
引用
收藏
页码:22 / 31
页数:10
相关论文
共 15 条
[1]  
Boom C. D., 2019, JOINT EUROPEAN C MAC, P454
[2]  
Briot JP, 2019, Arxiv, DOI arXiv:1709.01620
[3]  
Chandra A.L., 2018, Mcculloch-pitts neuron - mankinds first mathematical model of a biological neuron
[4]  
Ciaburro G., 2019, Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets
[5]  
Dieleman Sander, 2018, Advances in Neural Information Processing Systems, P7989
[6]  
Kedziora M., 2019, Int. J. Network Secur. Appl. (IJNSA), V11
[7]  
Kumar N.S., 2020, IFIP Advances in Information and Communication Technology book series (IFIPAICT), V578
[8]  
Lim H ..., 2017, P 18 INT SOC MUSIC I, P621, DOI [DOI 10.5281/ZENODO.1417327, 10.5281/zenodo.1417327]
[9]   Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network [J].
Liu, Hao-Min ;
Yang, Yi-Hsuan .
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, :722-727
[10]  
Liu Y., 2019, Python Machine Learning by Example, V2nd