基于人工智能音频处理技术优化音乐教学效果

Key Points

• This research aims to improve music education effectiveness through AI-driven audio processing technology.
• Introduced SMART-TUNE, an AI-based framework for real-time music training.
• Utilized convolutional neural networks (CNN) for pitch feature extraction and recurrent neural networks (RNN) for rhythm analysis.
• Trained and evaluated the model on an audio dataset from 180 participants, including professional musicians and students.
• Achieved pitch accuracy of 88.2%, rhythm accuracy of 91.7%, and error detection rate of 89.2%.
• Provided real-time feedback with low latency of 150-180 milliseconds.
• Generated a learner progress index of 0.76, indicating improved learning outcomes and user satisfaction.