The use of deep learning algorithm and digital media art in all-media intelligent electronic music system

Yingming Zheng

PLOS ONE, 2020, vol. 15, issue 10, 1-16

Abstract: In the development of digital media art, to explore the preliminary application of deep learning method in intelligent electronic music system, and promote the integration of deep learning method and digital media technology, thus providing a direction for the development of all media intelligent system, based on deep deterministic policy gradient (DDPG), to solve the multi-task problem in intelligent system, a multi-task learning-based DDPG algorithm (M-DDPG) is proposed. Furthermore, a DDPG algorithm based on hierarchical learning (H-DDPG) is proposed for the hierarchical analysis of images in intelligent system. Aiming at the problem of image classification in intelligent system, through the setting of simulation environment, the application effect of several algorithms in intelligent electronic music system is evaluated. The results show that: M-DDPG algorithm can more accurately complete the operation of related tasks, the reward received by the intelligent system is more than 0.35, and the test results based on eight tasks are more accurate and effective. Even in the case of task error, the algorithm still shows good training results. H-DDPG algorithm has good effect for complex task processing. The accuracy rate of task test corresponding to intelligent system in different scenarios is above 95%, which is better than other conventional algorithms in task test; the self-reinforcement network algorithm can promote the improvement of image classification effect. Several algorithms proposed show excellent performance in image processing of intelligent system, and have great application potential.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0240492

DOI: 10.1371/journal.pone.0240492

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