Research on personalized music education audio signal processing technology based on deep learning

With the rapid development of information technology, deep learning shows great potential in the field of audio signal processing, especially in personalized music education, which can provide students with customized, real-time feedback learning experience. The purpose of this paper is to study the audio signal processing technology based on deep learning and explore its application and implementation in personalized music education. The article analyzes the main challenges and development trends of audio signal processing in current music education, focusing on the key technologies of deep learning in audio feature extraction, classification, enhancement, and timing data processing. This paper proposes an audio processing framework based on the combination of deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN) for the implementation of audio signal analysis and processing in personalized music education system. The effectiveness of the proposed technique is verified through experiments, and the impact of different audio processing techniques on students' learning outcomes is analyzed. The application of deep learning in audio signal processing not only improves the degree of personalization of music learning, but also provides new ideas and technical support for the future development of music education.