RETRACTED: Music genre selection based on computer data analysis for user preference using fuzzy classification by deep learning model.

The article focuses on music genre classification based on user preferences using advanced machine learning techniques, specifically a convolutional belief transfer Gaussian model (CBTG) and a fuzzy recurrent adversarial encoder neural network (FRAENN). It addresses challenges in automatic music retrieval and classification by transforming audio signals into uniform representations and employing feature selection and dimensionality reduction to improve classification accuracy. The study utilizes well-known datasets such as GTZAN and Free Music Archive (FMA), demonstrating that the proposed models outperform existing methods like CNN and LDA in metrics including training accuracy, mean average precision, F-1 score, RMSE, and AUC. The research highlights the importance of efficient feature extraction and classification in large-scale digital music libraries to enhance music recommendation systems, while acknowledging ongoing difficulties such as data sparsity and the ambiguous nature of musical genres.