Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores

Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and extracting music score information in editable form from image data is currently a challenge. An improved convolutional neural network for musical score recognition is proposed in this paper. Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure. Use additional branch paths to fuse shallow image details, shallow texture features that are beneficial to small objects, and high-level features of global information, enrich the multi-scale semantic information of the model, and alleviate the problem of the lack of multiscale semantic information in the model. Poor recognition performance is caused by semantic information. By comparing the recognition effects of other models, the experimental results show that the proposed musical score recognition model has a higher recognition accuracy and a stronger generalization performance. The improved generalization performance allows the musical score recognition method to be applied to more types of musical score recognition scenarios, and such a recognition model has more practical value.