A Deep Learning-Based Piano Music Notation Recognition Method

In the era of rapid development of computer technology, piano music notation and electronic synthesis system can be established using computer technology, and the basic laws of music score can be analyzed from the perspective of image processing, which is of a great significance in promoting piano improvement and research and development, etc. In this paper, the Beaulieu analysis method is used to analyze the piano music notation and electronic synthesis system module. For piano sheet music, sheet music recognition is the main problem in the whole system. Through the digital recognition method, the piano sheet music feature matrix is extracted to get the piano sheet music multiplication frequency points and the envelope function needs to be extracted for better electronic synthesis of piano sheet music. The envelope function can represent the relationship between piano sound intensity and time change and finally achieve the recognition of the piano score. We extract the music information from the digital score, thus converting the music information into MIDI files, reconstructing the score, and providing an audio carrier for the score transmission. The experimental results show that the system has a correct rate of 94.4% in extracting music information from piano scores, which can meet the needs of practical applications and provide a new way for music digital libraries, music education, and music theory analysis.