Optical score recognition is a critical technology for retrieving music information, and note recognition is a critical component of score recognition. This article evaluates and discusses the current state of research on important technologies for score recognition. To address the issues of low note recognition accuracy and intricate steps in the present music score image, a deep learning-based music score recognition model is proposed. The model employs a deep network, accepts the entire score image as input, and outputs the note's time value and pitch directly. Experiments on music scores demonstrate that the method described in this study has a high note identification accuracy of 0.95 for time values and 0.97 for pitch, which is suitable for composition.

Traditionally, music was considered an analog signal that had to be made by hand. In recent decades, music has been highlighted by technology that can autonomously compose a suite of music without any human interaction. To achieve this goal, this article suggests an autonomous music composition technique based on long short-term memory recurrent neural networks. Firstly, the music collection is split into music sequences based on unit time, and the Meier cepstrum coefficients of music audio are retrieved as features during music preprocessing. Secondly, the training samples composed of feature vectors processed by data were trained and predicted by short- and long-term memory models. Finally, the generated music sequence is spliced and fused to get new music. This article designs and performs experiments to demonstrate that our results are promising. From experimental results, this work gained that our model has the maximum accuracy of 99% and the lowest loss rate of 0.03.