Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network.

Yuanyuan Zhang,Bai Yuan Ding

doi:10.1155/2021/4953288

Yuanyuan Zhang, Bai Yuan Ding

Open Access

https://doi.org/10.1155/2021/4953288

Copy DOI

Abstract

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users' preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

Highlights

Music is one of the popular entertainment media in the digital age
Is paper is divided into five parts. e first part expounds the research background that piano music is favored by most listeners and performers in the category of digital music. e second part describes the recommendation of the piano score and the research of related algorithms. e third part introduces the piano music recommendation system based on the convolutional neural network (CNN) and expounds the personalized recommendation system under big data and the recommendation algorithm based on deep learning
Under the RMSProp and Adam optimal controllers, the classification accuracy is increased by 2.0% and 1.25%, respectively. erefore, the note feature can improve the classification effect of the CNN model on the spectrum to a certain extent

Summary

Introduction

Music is one of the popular entertainment media in the digital age. As the product of human creativity, music expresses thoughts and emotions in the form of sound, including melody, tone, and rhythm. ere are many types of music, such as pop music, rock music, jazz, blues, and ballads. Music image analysis and recommendation system can be divided into three main parts: user, item, and useritem matching algorithm. The graphic representation method and feature extraction method of frequency domain features and note features of piano music are proposed, and the piano music image analysis and recommendation system based on the CNN classification is realized. E third part introduces the piano music recommendation system based on the CNN and expounds the personalized recommendation system under big data and the recommendation algorithm based on deep learning. It studies the spectrum representation and feature extraction of piano music.

Related Works

Piano Music Recommendation System Based on the CNN

Results and Discussion

Conclusion