Design of music training assistant system based on artificial intelligence

Authors

  • Hua Zhihan Shijiazhuang University of Applied Technology
  • Liang Yuan Shijiazhuang University of Applied Technology
  • Tao Jin Ordos Institute of Technology

DOI:

https://doi.org/10.4108/eai.11-2-2022.173450

Keywords:

Artificial intelligence, Music training, Assistance system, Audio acquisition module, Music signal, Radial basis function

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.

References

[ESLING P, MASUDA N, BARDET A, et al. Universal audio synthesizer control with normalizing flows[J]. Applied Sciences, 2019, 10(1):302.

PITT I L. Life cycle effects of technology on revenue in the music recording industry 1973–2017[J]. SN Business & Economics, 2020, 1(1): 9-10.

ANUFRIEVA N I, KAMENETS A V, PEREVERZEVA M V, et al. Features of the application of art-therapeutic and gaming technology based on folk music in rehabilitation and socialization of children with health limitations[J]. Open Access Macedonian Journal of Medical Sciences, 2020, 8(E):

- 381.

REBROVA O. Artistic and aesthetic innovations of art education used while training future teachers of music and choreography[J]. Scientific bulletin of South Ukrainian National Pedagogical University named after K D Ushynsky, 2019, 4(129):13-25.

DUIC L. Choose music! a consulting and training strategy for admission to higher education in music[J]. Review of Artistic Education, 2019, 17(1):40-46.

HU W J. Algorithm for sampling outliers in imbalanced data sets of artificial intelligence[J]. Computer Simulation, 2019, 37(11):324-328.

WOLFFENBÜTTEL C R. Music education training for teachers[J]. Creative Education, 2019, 10(10):2101-2110.

LIU S, WANG S, LUI X, et al. Fuzzy Detection aided Real-time and Robust Visual Tracking under Complex Environments[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(1):90-102.

PEREVERZEVA M V, OREKHOVA O G. Music hermeneutics, semantics, semiotics and their role in professional training of the musician[J]. Uchenye Zapiski RGSU, 2019, 18(4):124-130.

LIU S, LIU X, WANG S, et al. Fuzzy-Aided Solution for Out-of-View Challenge in Visual Tracking under IoT Assisted Complex Environment[J]. Neural Computing & Applications, 2021, 33(4): 1055-1065

GUO J, LIU J. Optimal system design of language training strategy based on artificial intelligence[J]. Journal of Intelligent and Fuzzy Systems, 2020, 40(4):1-11.

TAKAMA Y, ZHANG J C, SHIBATA H. Context-aware music recommender system based on implicit feedback[J]. Transactions of the Japanese Society for Artificial Intelligence, 2021, 36(1):1-10.

DING Y. Performance analysis of public management teaching practice training based on artificial intelligence technology[J]. Journal of Intelligent and Fuzzy Systems, 2020, 40(5):1-14.

KOEMPEL F. From the gut? questions on artificial intelligence and music[J]. Queen Mary Journal of Intellectual Property, 2020, 10(4):503-513.

AVDEEFF M. Artificial intelligence & popular music: skygge, flow machines, and the audio uncanny valley[J]. Arts, 2019, 8(4):130.

WILLIAMS R. Artificial intelligence assistants in the library siri, alexa, and bevond[J]. Online, 2019, 43(3):10-15.

STERNE J, RAZLOGOVA E. Machine learning in context, or learning from landr: artificial intelligence and the platformization of music mastering[J]. Social Media + Society, 2019, 5(2):205630511984752.

JABADE V, DESHPANDE V, ADITYA K. Music generation and song popularity prediction using artificial intelligence - an overview[J]. International Journal of Computer Applications, 2019, 182(50):33-39.

SHUAI L, TENGHUI H, JIANHUA D. A Survey of CRF Algorithm Based Knowledge Extraction of Elementary Mathematics in Chinese[J]. Mobile Networks & Applications, 2021, 26(5):1891-1903

PENG G, JINGYI :L, SHUAI L. An Introduction to Key Technology in Artificial Intelligence and big Data Driven e-Learning and e-Education[J]. Mobile Networks & Applications, 2021, 26(5): 2123-2126

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Published

11-02-2022

How to Cite

1.
Zhihan H, Yuan L, Jin T. Design of music training assistant system based on artificial intelligence. EAI Endorsed Scal Inf Syst [Internet]. 2022 Feb. 11 [cited 2024 Nov. 14];9(6):e2. Available from: https://publications.eai.eu/index.php/sis/article/view/346

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