Database design of regional music characteristic culture resources based on improved neural network in data mining

Original Article


With the improvement of living standards, Music Appreciation Art has gradually become the pursuit of people. As an important part of music resources, regional music is an indispensable treasure of music appreciation art. Regional music culture with its unique charm is constantly affecting modern people’s music appreciation ability. Fully learning regional music culture is the key to carry forward traditional culture. However, as an important part of the cultural treasure house, regional music characteristic culture resources lack reasonable digital storage. Therefore, reasonable and sufficient mining of regional music characteristic cultural resource data is of great significance to the protection of regional characteristic culture. In this paper, the database of regional culture and music characteristic resources is establishedby data mining technology. At the same time, combined with the improved BP neural network model to classify the regional characteristic music andcultural resources data. A set of database including classification, search, audition and storage is established in order to protect and spread the regional music characteristic cultural resources. Finally, it provides new ideas for cultural heritage and cultural heritage.


Data mining Regional music characteristic culture resources Database design Improved BP neural network 


Funding information

This work is supported by the Humanities and Social Sciences Program of Ministry of Education in 2017 (No. 17YJC890049), supported by the Fund for Shanxi “1331Project” Key Innovative Research Team (No. 1331KIRT).


  1. 1.
    Light D (2007) Dracula tourism in Romania Cultural identity and the state[J]. Ann Tour Res 34(3):746–765. CrossRefGoogle Scholar
  2. 2.
    Costa YMG, Oliveira LS, Koerich AL et al (2012) Music genre classification using LBP textural features[J]. Signal Process 92(11):2723–2737. CrossRefGoogle Scholar
  3. 3.
    Han BJ, Rho S, Jun S et al (2010) Music emotion classification and context-based music recommendation[J]. Multimed Tools Appl 47(3):433–460. CrossRefGoogle Scholar
  4. 4.
    Rocamora M, Cancela P, Pardo A (2014) Query by humming: Automatically building the database from music recordings[J]. Pattern Recogn Lett 36:272–280. CrossRefGoogle Scholar
  5. 5.
    Thornburg G, Oskins WM (eds) (2012) Matching music: clustering versus distinguishing records in a large database[J]. OCLCS&S: IDLP 28(1):32–42. CrossRefGoogle Scholar
  6. 6.
    Yue Y (2014) Database Design of Pop Music Website Development[J]. Appl Mech Mater 687-691:3023–3026. CrossRefGoogle Scholar
  7. 7.
    Edward MGC (2017) Of Programs and Prima Donnas: Investigating British Music with the Musical Festivals Database[J]. Notes 73(3):432–472. CrossRefGoogle Scholar
  8. 8.
    Beheshti S (2010) The case for a database of musical repertoire[J]. Int J Music Educ 28(4):369–379. CrossRefGoogle Scholar
  9. 9.
    Hu W, Tan BY (2016) Data Mining and Big Data[J]. IEEE TOK&DE 26(1):97–107. CrossRefGoogle Scholar
  10. 10.
    Xiao-Zhu S (2016) Department C. The compilation, classification and disseminate of minority music: based on the basis of database[J]. GES 37(180):73–76. CrossRefGoogle Scholar
  11. 11.
    Martens D, Baesens B, Fawcett T (2011) Editorial survey: swarm intelligence for data mining[J]. Mach Learn 82(1):1–42. MathSciNetCrossRefGoogle Scholar
  12. 12.
    Howard K (2018) The emergence of children’s multicultural sensitivity: an elementary school music culture project[J]. J Res Music Educ 66(3):261–277. CrossRefGoogle Scholar
  13. 13.
    Jian-Xiong GE (2012) Regional Culture of China[J]. GCAH 7-11.
  14. 14.
    Zheng L, Hu W, Min Y (2015) Raw wind data preprocessing: a data-mining approach[J]. IEEE TOSE 6(1):11–19. CrossRefGoogle Scholar
  15. 15.
    Gao W, Wang WF (2017) The fifth geometric-arithmetic index of bridge graph and carbon nanocones[J]. JODEAA 23(1-2SI):100–109. MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Santi D, Magnani E, Michelangeli M, Grassi R, Vecchi B, Pedroni G, Roli L, de Santis MC, Baraldi E, Setti M, Trenti T, Simoni M (2018) Seasonal variation of semen parameters correlates with environmental temperature and air pollution: a big data analysis over 6 years[J]. Environ Pollut 235:806–813. CrossRefGoogle Scholar
  17. 17.
    Saraf Esmaili S, Maghooli K, Nasrabadi AM (2018) A new model for face detection in cluttered backgrounds using saliency map and c2 texture features[J]. Int J Comput Appl 40(4):214–222Google Scholar
  18. 18.
    Aurlien H, Gjerde IO, Aarseth JH et al (2004) EEG background activity described by a large computerized database[J]. Clin Neurophysiol 115(3):665–673. CrossRefGoogle Scholar
  19. 19.
    Peral J, Maté A, Marco M (2017) Application of data mining techniques to identify relevant key performance indicators[J]. CS&I 50:55–64. CrossRefGoogle Scholar
  20. 20.
    JIANG X, Li S (2018) BAS: beetle antennae search algorithm for optimization problems[J]. IJORAC 1(1):1–5. MathSciNetCrossRefGoogle Scholar
  21. 21.
    Nobukawa T, Nomura T (2017) Digital super-resolution holographic data storage based on Hermitian symmetry for achieving high areal density.[J]. Opt Express 25(2):1326. CrossRefGoogle Scholar
  22. 22.
    Khramtsova EA, Stranger BE (2017) Assocplots: a python package for static and interactive visualization of multiple-group GWAS results[J]. Bioinformatics 33:432–434. CrossRefGoogle Scholar
  23. 23.
    Ezenwoke A, Daramola O, Adigun M (2018) Qos-based ranking and selection of saas applications using heterogeneous similarity metrics[J]. JOCC 7(1):–12.
  24. 24.
    Afolabi AO, Fagbenle OI, Mosaku TO (2017) Characteristics of a web-based integrated material planning and control system for construction project delivery[J]. RAIISAT 20-30. Google Scholar
  25. 25.
    Zickler AM, Hampp S, Messiaen L, Bengesser K, Mussotter T, Roehl AC, Wimmer K, Mautner VF, Kluwe L, Upadhyaya M, Pasmant E, Chuzhanova N, Kestler HA, Högel J, Legius E, Claes K, Cooper DN, Kehrer-Sawatzki H (2012) Characterization of the nonallelic homologous recombination hotspot PRS3 associated with type-3 NF1 deletions[J]. Hum Mutat 33(2):372–383. CrossRefGoogle Scholar
  26. 26.
    Ramos NMM, Almeida RMSF, Simōes ML et al (2017) Knowledge discovery of indoor environment patterns in mild climate countries based on data mining applied to in-situ measurements[J]. Sustain Cities Soc 30:37–48. CrossRefGoogle Scholar
  27. 27.
    Chien CF, Huang YC, Hu CH (2017) A hybrid approach of data mining and genetic algorithms for rehabilitation scheduling.[J]. Int J Manuf Technol Manag 16(1):76–100. CrossRefGoogle Scholar
  28. 28.
    Gao W, Wang W (2017) New isolated toughness condition for fractional (g, f, n) - critical graph[J]. Colloq Math 147(1):55–65MathSciNetCrossRefGoogle Scholar
  29. 29.
    Irain M, Jorda J, Mammeri Z (2017) Landmark-based data location verification in the cloud: review of approaches and challenges[J]. JOCC 6(1).Google Scholar
  30. 30.
    Bagui S (2006) Rules for Migrating from Entity Relationship (ER) Diagrams to Object Relationship (OR) Diagrams[J]. Comput Lett 2(4):177–191. CrossRefGoogle Scholar
  31. 31.
    Bossi L, Bertino E, Hussain S (2017) A system for profiling and monitoring database access patterns by application programs for anomaly detection[J]. IEEE Trans Softw Eng PP(99):1–1. CrossRefGoogle Scholar
  32. 32.
    Safarzadeh MS, Howard SM, Miller JD (2018) Analysis and visualization of enargite and tennantite roasting using Cu-As-S-O system predominance volume diagrams[J]. Vacuum 156:78–90. CrossRefGoogle Scholar
  33. 33.
    Wu J, Wei W, Zhang L, Wang J, Damaševičius R, Li J, Wang H, Wang G, Zhang X, Yuan J, Woźniak M (2019) Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation[J]. IEEE Access 7(1):23109–23119CrossRefGoogle Scholar
  34. 34.
    Yu-Zhou L, Zhong-Wei J, Qi S et al (2016) A design of sleeping conditions monitoring system based on SVM[J]. CE&S DACDSSS 37(10):64–67. CrossRefGoogle Scholar
  35. 35.
    Jin J, Mi W (2019) An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay[J]. Discrete and Continuous Dynamical Systems Series S 12(4-5):1101–1115MathSciNetCrossRefGoogle Scholar
  36. 36.
    Roul JN, Maity K, Kar S et al (2017) Optimal control problem for an imperfect production process using fuzzy variational principle[J]. J Intell Fuzzy Syst 32(1):565–577CrossRefGoogle Scholar
  37. 37.
    Juan LI, Mingquan Z, Peng LI (2011) Music database construction based on MIDI melody feature extraction[J]. CE&A 47(26):124–128. CrossRefGoogle Scholar
  38. 38.
    Nettl B (2017) Have you changed your mind? Reflections on sixty years in ethnomusicology[J]. AM 89(1):págs. 45-65. CrossRefGoogle Scholar
  39. 39.
    Rauscher B, Heigwer F, Breinig M, Winter J, Boutros M (2017) GenomeCRISPR - a database for high-throughput CRISPR/Cas9 screens[J]. Nucleic Acids Res 45(Database issue):D679–D686. CrossRefGoogle Scholar
  40. 40.
    Falade B (2018) Cultural differences and confidence in institutions: comparing Africa and the USA[J]. S Afr J Sci, 114(5/6). doi:
  41. 41.
    Brzeziński DW (2018) Review of numerical methods for numilpt with computational accuracy assessment for fractional calculus[J]. AM&N 3(2):487–502MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Academy of Music, Shanxi UniversityTaiyuanChina

Personalised recommendations