Piano score recognition is one of the important research contents in the field of music information retrieval, and it plays an important role in information processing. In order to reduce the influence of vocals on the progress of piano notes and restore the harmonic information corresponding to piano notes, the article models the harmonic information and vocal information corresponding to piano notes in the frequency spectrum. We use the phase space reconstruction method to extract the nonlinear feature parameters in the note audio and use some of the parameters as the training set to construct the support vector machine (SVM) classifier and the other part as the test set to test the recognition effect. Therefore, the method of adaptive signal decomposition and SVM is introduced into the signal preprocessing link, and the corresponding recognition process is established. In order to improve the performance of the support vector machine, the article uses measurement learning method to obtain the measurement learning and uses the measurement learning to replace the Euclidean distance of the Gaussian kernel function of the support vector machine. The SVM method of adaptive signal decomposition and the SVM method of principal component analysis are introduced into the preprocessing process of the note signal, and then the preprocessed signal is reconstructed in phase space, and the corresponding recognition process is established. The method of directly reconstructing the phase space of the original signal has higher accuracy and can be applied to the note recognition of continuous music segments. The final experimental results show that, compared with the current popular piano score recognition algorithm, the recognition accuracy of the proposed piano score recognition algorithm is improved by 3.5% to 12.2%.

The paper follows the experimental study of "the effect of computer-assisted instruction on piano teaching: An experimental study of students of biological sciences". It analyzes the results, which show a significant increase in practice time and success rate. Scientists from both universities have been working on a project to see if computer-assisted instruction would be useful for music education. They ran an experiment with students to see its effects. They found that it had greater effects on some types of learning than others. The paper summarizes the findings and talks about implications for future research in this field as well as practical applications for computer-assisted instruction in various fields, including education. Evaluation was based on three areas: practice, piano playing, and music appreciation. Students were divided into two groups: computer students and students without computers. The research team had expected that by using the computer, students will be able to practice longer and improve more quickly. The experiment also supposed that the additional practice would affect their concentration and listening skills in playing well-known pieces of music.