Musical Genres Classification Utilizing the Pre-trained ResNet50 CNN Model and Deep Learning Techniques

This research investigates the application of the ResNet50 Convolutional Neural Network (CNN) within a ResNet50 model framework for the purpose of classifying musical genres. The objective is to enhance the accuracy and efficiency of automated music genre categorization systems through the utilization of deep learning techniques. The proposed model employs a methodology that processes raw audio data, involving the extraction of relevant innovative features through convolutional layers. These layers are designed to capture hierarchical patterns inherent to specific genres. The incorporation of the ResNet50 architecture in machine learning facilitates the capture of temporal relationships, allowing the model to recognize subtle nuances and variations in musical compositions. The study utilizes a diverse dataset encompassing multiple genres to enhance the robustness and adaptability of the model. The primary goal is to validate the effectiveness of the CNN ResNet50 Model in accurately classifying musical genres. Through rigorous experimentation and assessment, this research aims to contribute significantly to the advancement of automated music analysis and classification systems. The findings of this study have noteworthy implications for various applications, including music recommendation systems, content tagging, and music streaming services.