Open AccessResearch on automatic recognition algorithm of piano music based on convolution neural network
Author(s) -
Wu Ran
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012086
Subject(s) - window function , convolutional neural network , computer science , artificial neural network , convolution (computer science) , window (computing) , artificial intelligence , pattern recognition (psychology) , piano , algorithm , deep learning , feature (linguistics) , speech recognition , computer vision , acoustics , linguistics , philosophy , physics , filter (signal processing) , operating system
Convolutional neural network is a typical deep neural network, which combines the advantages of deep learning and image processing, improving the accuracy of feature recognition and greatly reducing the calculation of neural network. Window function is a signal with finite width in time domain. In signal processing, the use of window function can reduce the leakage of spectrum. Combining the window function and convolution neural network, the effective input in the model can be increased, and the accuracy and speed can be improved. In this paper, the music score of piano music is processed by window function as the input image of convolutional neural network. Through the analysis of the results, it is found that the algorithm has a 25.8% increase in rate compared with the traditional recognition method in rate, and the average value of F value reaches 89.24% in the accuracy test. The test results show that the piano music recognition rate of the algorithm has been greatly improved, and good results have been achieved in accuracy.