Analysis of Two-Piano Teaching Assistant Training Based on Neural Network Model Sound Sequence Recognition

Area of Science:

• Music Education
• Artificial Intelligence
• Signal Processing

Background:

• Piano study is increasingly valued for spiritual enrichment.
• The two-piano method is a unique teaching approach.
• Noise and reverberation degrade sequence recognition accuracy in two-piano playing.

Purpose of the Study:

• To develop an AI-based auxiliary training analysis system for two-piano playing.
• To improve the accuracy of sequence recognition in challenging acoustic environments.
• To enhance piano player training efficiency and performance quality.

Main Methods:

• A multitask preprocessing method combining speech enhancement and detection was used for deep neural network training.
• A convolutional neural network (CNN) was employed for end-to-end recognition.
• A phonological sequence model was used to correct initial recognition results.
• A speech enhancement front-end module improved articulation for sequence recognition in noise.

Main Results:

• The proposed system significantly improves training efficiency and player performance quality compared to traditional methods.
• The system demonstrates effectiveness in sequence recognition even under noisy and reverberant conditions.
• The AI-based approach enhances the scientific rigor of two-piano training.

Conclusions:

• The developed system offers a novel and scientific approach to two-piano training.
• Artificial intelligence technology can be effectively applied to improve piano teaching methods.
• The system addresses limitations of traditional training by enhancing accuracy and efficiency.