Vocal performance evaluation of the intelligent note recognition method based on deep learning

This study aims to optimize the ability of note recognition and improve the accuracy of vocal performance evaluation. Firstly, the basic theory of music is analyzed. Secondly, the convolutional neural network (CNN) in deep learning (DL) is selected to integrate gated recurrent units for optimization. Moreover, the attention mechanism is added to the optimized model to implement an intelligent note recognition model, and the results of note recognition are compared with those of common models. Finally, according to the results of audio signal classification, a vocal performance evaluation model optimized based on the attention mechanism is constructed. The accuracy of the model under different feature inputs is compared. The results indicate that different models show obvious differences in F-value, accuracy, precision, and recall. The attention mechanism-gated recurrent convolutional neural network (A-GRCNN) model performs best on all indicators. Specifically, this model's accuracy, recall, F-value, and precision reach 0.961, 0.958, 0.963, and 0.970. The incorporation of multiple feature inputs can remarkably enhance the accuracy of vocal performance evaluation, especially the combination of constant Q Transform features, which is the most outstanding. This study improves the accuracy and reliability of music information processing, promotes the application of DL technology in music, and contributes to optimizing vocal performance evaluation.