Title: Application of integrated image processing technology based on PCNN in online music symbol recognition training

Authors: Ting Zhang

Addresses: Academy of Arts, Shangluo University, Shaanxi, China

Abstract: To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Keywords: image processing; simplified symbolic music theory; symbol recognition; online training; skew correction; CNN.

DOI: 10.1504/IJWMC.2024.142069

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.4, pp.369 - 380

Received: 09 Nov 2023
Accepted: 01 Mar 2024
Published online: 07 Oct 2024 *

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