Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence

Area of Science:

• Artificial Intelligence in Education
• Computational Musicology
• Machine Learning for Performance Assessment

Background:

• Traditional music education often lacks personalized feedback mechanisms.
• Assessing musical performance requires nuanced analysis of sequential data.
• Existing digital tools may not fully capture the complexities of musical expression.

Purpose of the Study:

• To develop an intelligent digital music teaching model using AI and Long Short-Term Memory (LSTM) networks.
• To enhance personalized assessment and feedback in music education.
• To evaluate the effectiveness and usability of the proposed AI model.

Main Methods:

• Implementation of a music evaluation module using a three-layer Bidirectional LSTM (Bi-LSTM) with an attention mechanism.
• Processing Musical Instrument Digital Interface (MIDI) data to capture long-term sequential features.
• Comparative experimental analysis against multiple models and a usability survey with teachers and students.

Main Results:

• The three-layer Bi-LSTM model achieved a final accuracy of 91.9%, outperforming other models.
• Superior classification accuracy and stability demonstrated by precision (0.87), recall (0.854), and F1-score (0.86).
• Usability survey results indicated high satisfaction (above 4.0) from both teachers and students regarding teaching effectiveness, user experience, and engagement.

Conclusions:

• The proposed AI-driven model offers a novel approach for personalized music education.
• Deep network structures, specifically the three-layer Bi-LSTM, are effective for complex music assessment tasks.
• The model shows excellent applicability and potential for widespread adoption in music teaching practices.