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Research on Music Style Classification Based on Deep Learning.

Wei Wang1, Mishal Sohail1

  • 1School of Marxism, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China.

Computational and Mathematical Methods in Medicine
|January 28, 2022
PubMed
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This study introduces a deep learning approach for music style classification, achieving over 93.3% accuracy. The novel method enhances classification stability and reduces processing time for music genre identification.

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Area of Science:

  • Computer Science
  • Music Information Retrieval
  • Artificial Intelligence

Background:

  • Current music style classification methods rely on feature extraction (e.g., rhythm, timbre) and traditional classifiers, often resulting in suboptimal accuracy and stability.
  • Limitations in feature extraction significantly impact the performance of music genre classification systems.
  • There is a need for more robust and accurate methods for automatic music style identification.

Purpose of the Study:

  • To investigate a deep learning-based method for music style classification.
  • To improve the accuracy and stability of music genre classification.
  • To reduce the computational overhead associated with music style identification.

Main Methods:

  • Music signals were framed using filters and Hamming windows.
  • Mel-Frequency Cepstral Coefficients (MFCC) were extracted via discrete Fourier transform.
  • A hybrid Convolutional Recurrent Neural Network (CNN-RNN) architecture was designed and trained for classification.

Main Results:

  • The proposed deep learning model achieved a classification accuracy of at least 93.3%.
  • The method demonstrated significantly reduced classification time overhead compared to existing approaches.
  • The classification results exhibited high stability and reliability.

Conclusions:

  • Deep learning, specifically a CNN-RNN architecture, offers a superior approach to music style classification.
  • The developed method provides accurate, stable, and efficient music genre identification.
  • This research contributes a reliable deep learning solution for music information retrieval tasks.