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Learning Chinese Classical Music with the Aid of Soundscape by Using Intelligent Network.

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Music Morphology Interaction under Artificial Intelligence in Wireless Network Environment.

LiLan Zhang1

  • 1Art Department, Puyang Vocational and Technical College, Puyang 457000, Henan, China.

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Summary

This study introduces a novel method for classifying music morphology using neural networks and the Relief algorithm. The developed model achieves 92% accuracy, outperforming traditional methods for music information retrieval.

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Area of Science:

  • Computer Science
  • Music Information Retrieval
  • Machine Learning

Background:

  • Music serves as a primary information carrier, necessitating accurate classification of its emotional expression and morphology.
  • Classifying music accurately remains a challenge, particularly in digital environments.
  • Understanding music morphology is crucial for effective information retrieval and analysis.

Purpose of the Study:

  • To describe the concept and feature extraction strategies for music morphology.
  • To introduce feature extraction and morphological classification elements for digital music.
  • To develop and validate a music morphology recognition and classification model.

Main Methods:

  • Concept and feature extraction strategies for music morphology were detailed.
  • Digital music's feature extraction and morphological classification elements were introduced.
  • A neural network combined with the Relief algorithm was employed for music morphology recognition and classification, with audio data processed through the neural network and Relief algorithm.

Main Results:

  • The Relief algorithm's classification accuracy, influenced by the number of iterations, was verified.
  • The model achieved a classification accuracy of 78.958% based on the number of iterations.
  • The proposed model demonstrated a recognition accuracy of 92%, significantly outperforming traditional statistical analysis methods.

Conclusions:

  • The developed model effectively classifies music morphology.
  • This research provides a theoretical foundation for music morphology recognition within wireless network environments.
  • The findings highlight the potential of machine learning approaches in music information processing.