[Retracted] Visual Classification of Music Style Transfer Based on PSO‐BP Rating Prediction Model

Abstract

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

Application of BP Neural Network in Matching Algorithm of Network E-Commerce Platform

“…For example, if the initial weight and threshold are too large, the input falls into the saturation region of the S-type transfer function after the network weighting calculation, resulting in a very small gradient, which makes the adjustment process difficult to continue. Therefore, it is generally expected that after the initial weighting calculation of the network, the output of neurons will approach zero as much as possible, so that the weight of each neuron can be adjusted at the place where their S-type activation function changes the most [16]. Therefore, it generally takes the random number with the initial weight between [−1, 1].…”

Method of rapid search of audio recording fragment (Ukr)

“…Для кожного фрейму виконується спектральний аналіз, на основі якого обчислюється значення вектора параметрів (параметризація). Серед різних параметрів обрано мел-частотні кепстральні коефіцієнти (MFCC -Mel Frequency Cepstral Coefficients), які вперше було запропоновано використовувати у системах розпізнавання мови і диктора [1], надалі вони отримали широке застосування у процесі інформаційного пошуку музики (Music information retrieval, MIR) [2]. У результаті параметризації отримуємо опис МТ як файл, що містить вектори MFCC.…”

Simulation of music style classification model based on support vector machine algorithm

“…However, it is often difficult to find the music with the style you like quickly and efficiently from the massive music data, which requires the construction of an automatic music style classification system with good performance. Therefore, more and more related technicians are involved in the research of the automatic music style classification system 5 .…”