Practice and Exploration of Music Solfeggio Teaching Based on Data Mining Technology

Area of Science:

• Music Education
• Data Mining
• Signal Processing

Background:

• The solfeggio method is crucial for music learning and professional training.
• Existing music curriculum models can benefit from advanced analytical techniques.
• Data mining offers potential for optimizing music education and analysis.

Purpose of the Study:

• To analyze a music curriculum model using data mining.
• To develop an efficient music style classification method.
• To improve the accuracy and efficiency of music analysis.

Main Methods:

• Utilized data mining technology for curriculum modeling.
• Developed a music style classification method using FP Growth association rules.
• Applied a multirepetition structure model with Mel-frequency cepstral coefficients (MFCC).

Main Results:

• The FP Growth-based classification method significantly reduces computational overhead.
• Achieved approximately 33% faster execution time compared to other methods at 0.2% minimum support.
• The method effectively captures musical repeatability and enhances classification performance.

Conclusions:

• The proposed data mining approach offers an efficient solution for music style classification.
• This method can improve the technical aspects of music training and analysis.
• Further research can explore applications in music education and performance analysis.