Optimizing Music Genre Classification Using CNN Sequential Models and Deep Learning Techniques

This work classifies musical genres using convolutional neural networks (CNN) housed inside a sequential model framework. Deep learning methods are applied in this work to try to raise the accuracy and efficiency of automated music genre classification systems. The proposed model treats unprocessed audio data using a method. Using convolutional layers, meant to capture hierarchical patterns inherent in some genres, this process extracts pertinent innovative features. In machine learning, sequential architecture helps to acquire temporal relationships, hence the model can detect intricate subtleties and variations in musical compositions. The work accesses a heterogeneous dataset including many genres in order to increase the resilience and adaptability of the model. The aim of this work is to verify if the CNN Sequential Model can properly categorize musical genres. The work intends to greatly advance automated music analysis and classification systems by strict experimentation and evaluation. The results of this study have significant consequences for many various applications including safety-based content tagging for clean music to protect early childhood of children in school environments, music streaming services, and music recommendation systems.