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Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology.

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  • 1Cai Jikun Conservatory of Music, Minjiang University, Fuzhou, Fujian Province 350108, China.

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This study introduces an AI-powered piano scoring system using Big Data and BP neural networks. The system accurately evaluates performance, offering guidance for improvement and addressing piano education challenges.

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Area of Science:

  • Music Education Technology
  • Artificial Intelligence in Music
  • Computational Musicology

Background:

  • Current piano education lacks a comprehensive, scientific teaching model.
  • Existing methods struggle to meet evolving educational demands.
  • Piano scoring systems offer potential for supplementary teacher guidance.

Purpose of the Study:

  • To develop a scientific piano performance scoring model.
  • To address limitations in current piano education methodologies.
  • To explore the application of Big Data and neural networks in music assessment.

Main Methods:

  • Extraction of musical playing signal characteristics.
  • Development of a piano performance scoring model using Big Data.
  • Implementation of Backpropagation (BP) neural network technology for scoring.
  • Testing the model with famous piano works.

Main Results:

  • The model fairly and accurately scores piano performances.
  • It effectively evaluates players' skill levels.
  • The system provides valuable feedback for musical improvement.

Conclusions:

  • Big Data and BP neural networks optimize piano scoring systems effectively.
  • The developed model offers a new approach to music teaching and assessment.
  • This technology can help cultivate high-quality piano talents and alleviate teacher shortages.