sis 18: e44

Research Article

Design of music training assistant system based on artificial intelligence

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  • @ARTICLE{10.4108/eai.11-2-2022.173450,
        author={Hua Zhihan and Liang Yuan and Tao Jin},
        title={Design of music training assistant system based on artificial intelligence},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={2},
        keywords={Artificial intelligence, Music training, Assistance system, Audio acquisition module, Music signal, Radial basis function},
        doi={10.4108/eai.11-2-2022.173450}
    }
    
  • Hua Zhihan
    Liang Yuan
    Tao Jin
    Year: 2022
    Design of music training assistant system based on artificial intelligence
    SIS
    EAI
    DOI: 10.4108/eai.11-2-2022.173450
Hua Zhihan1, Liang Yuan2, Tao Jin3,*
  • 1: Department of Art and Design, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, China
  • 2: Department of Architectural Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, China
  • 3: Department of mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, 017010, China
*Contact email: jintao@oit.edu.cn

Abstract

In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.