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Neural Computing and Applications

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06.03.2021 | S.I: Cognitive-inspired Computing and Applications

Exploring online intelligent teaching method with machine learning and SVM algorithm

verfasst von: Wang Shuo, Mu Ming

Erschienen in: Neural Computing and Applications | Ausgabe 4/2022

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Abstract

In order to improve the effect of modern music teaching, this paper builds an intelligent music teaching system based on machine learning and SVM algorithm, innovates the music teaching process, and gradually expands from the simplest three-layer structure BPNN to a multi-layer structure. Moreover, this paper proposes a method of dividing the error by the proportion of each link's contribution to the error, and the idea that the error of the hidden layer node is the sum of the errors on each link during the forward propagation process. In addition, this paper combines the actual needs of music teaching to construct an intelligent music teaching system. Finally, this paper conducts training tests on music teaching data and sets up the experimental group and the control group to evaluate the system teaching effect based on actual needs. The research results show that the performance of the intelligent music teaching system constructed in this paper is good.

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Ni Y, McVicar M, Santos-Rodriguez R et al (2012) An end-to-end machine learning system for harmonic analysis of music. IEEE Trans Audio Speech Lang Process 20(6):1771–1783CrossRef

Sturm BL, Ben-Tal O, Monaghan Ú et al (2019) Machine learning research that matters for music creation: A case study. J New Music Res 48(1):36–55CrossRef

Ramírez J, Flores MJ (2020) Machine learning for music genre: multifaceted review and experimentation with audioset. J Intell Inf Syst 55(3):469–499CrossRef

Hewahi N, AlSaigal S, AlJanahi S (2019) Generation of music pieces using machine learning: long short-term memory neural networks approach. Arab J Basic Appl Sci 26(1):397–413CrossRef

Ali MA, Siddiqui ZA (2017) Automatic music genres classification using machine learning. Int J Adv Comput Sci Appl 8(8):337–344

Cai J, Luo J, Wang S et al (2018) Feature selection in machine learning: A new perspective. Neurocomputing 300(2):70–79CrossRef

Goetz JN, Brenning A, Petschko H et al (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81(1):1–11CrossRef

Xin Y, Kong L, Liu Z et al (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6(1):35365–35381CrossRef

Amershi S, Cakmak M, Knox WB et al (2014) Power to the people: The role of humans in interactive machine learning. AI Mag 35(4):105–120

10.

Coley CW, Barzilay R, Jaakkola TS et al (2017) Prediction of organic reaction outcomes using machine learning. ACS Cent Sci 3(5):434–443CrossRef

11.

Voyant C, Notton G, Kalogirou S et al (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105(2):569–582CrossRef

12.

Sieg J, Flachsenberg F, Rarey M (2019) In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J Chem Inf Model 59(3):947–961CrossRef

13.

Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inf J 26(1):264–286CrossRef

14.

Narudin FA, Feizollah A, Anuar NB et al (2016) Evaluation of machine learning classifiers for mobile malware detection. Soft Comput 20(1):343–357CrossRef

15.

Yao Q, Yang H, Zhu R et al (2018) Core, mode, and spectrum assignment based on machine learning in space division multiplexing elastic optical networks. IEEE Access 6(6):15898–15907CrossRef

16.

Jayasinghe U, Lee GM, Um TW et al (2018) Machine learning based trust computational model for IoT services. IEEE Trans Sustain Comput 4(1):39–52CrossRef

17.

Laird JE, Lebiere C, Rosenbloom PS (2017) A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Mag 38(4):13–26

18.

Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521(7553):452–459CrossRef

19.

Zappone A, Di Renzo M, Debbah M et al (2019) Model-aided wireless artificial intelligence: embedding expert knowledge in deep neural networks for wireless system optimization. IEEE Veh Technol Mag 14(3):60–69CrossRef

20.

Kumar PH, Mohanty MN (2016) Efficient feature extraction for fear state analysis from human voice. Indian J Sci Technol 9(38):1–11CrossRef

21.

Rhodes R (2017) Aging effects on voice features used in forensic speaker comparison. Int J Speech Lang Law 24(2):177–199

22.

Duong NQK, Duong HT (2015) A review of audio features and statistical models exploited for voice pattern Design. Comput Sci 3(2):36–39MathSciNet

23.

Sarria-Paja M, Senoussaoui M, Falk TH (2015) The effects of whispered speech on state-of-the-art voice based biometrics systems. Can Conf Electr Comput Eng 2015(1):1254–1259

Titel: Exploring online intelligent teaching method with machine learning and SVM algorithm
verfasst von: Wang Shuo
Mu Ming
Publikationsdatum: 06.03.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05846-6

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